Systems and Methods for Agricultural Optimization

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 63/274,668 filed Nov. 2, 2021, entitled “Bulbosa 7PB55 Cultivar Cover Crop Systems and Methods for Northern and Central California Wine Grape Crops” and U.S. Provisional Patent Application Ser. No. 63/274,625 filed Nov. 2, 2021, entitled “System and/or Methods for Cover Crop Application, Determining Cover Crop Performance and/or Carbon Credits Generation from Cover Crop Application”, the entirety of each of which is incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates to systems and/or methods for agricultural optimization. Particular embodiments of systems and methods of the present disclosure provide environmental optimization and methods, particularly the area of soil carbon enhancement or increase and/or biomass accumulation in previously unused biomass accumulation regions within farm parcels.

BACKGROUND

Agriculture is intrinsic to global health and prosperity while simultaneously putting an enormous strain on the planet's water, soil, and human resources, and contributing a significant fraction of global greenhouse gas emissions. However, agricultural lands also have the capacity to reverse or mitigate adverse environmental impacts when subjected to interventions that purposefully reduce an associated pressure on compromised environmental resources. These interventions can shift the environmental impacts of an agricultural system from extractive to regenerative.

Cover crops have been used to enhance the productivity of commodity crops. Cover cropping is an intervention in which complementary, unharvested vegetation is planted in an agricultural system for the purpose of enhancing its agro-economy through any combination of lowering required inputs, mitigating agronomic pressures, or regenerating environmental attributes such as soil health. In specialty crop systems where the commodity crop is planted in rows, cover crops can be planted in the alleyways between rows and sometimes underneath the trees or vines of the cash crop. Cover crops can be annual or perennial and multiple species can be planted as a mix in the same space. As farmers weigh costs with benefits and consider the barriers to trying unfamiliar practices, adoption rates for cover cropping remain low in specialty crop systems. Perceived competition for water and soil resources are significant drawbacks, as is the cost of clearing and re-seeding annual varieties each year.

Despite low adoption rates, the upside of cover cropping is very favorable to certain types of farming operations, such as those practicing no-till. In no-till systems, soil is left undisturbed by tillage, providing a variety of agronomic benefits including erosion control. A cover crop can raise soil carbon content by absorbing the greenhouse gas carbon-dioxide through photosynthesis and depositing the resulting organic carbon byproducts into the soil through its root systems and by interaction with other biological processes. This soil carbon can be temporarily removed from the atmosphere when combined with the practice of no-till. A need exists for a trustworthy cover cropping system that does not impede agricultural productivity and complements existing biological and agronomic farm schedules while providing an incentive for implementation by farmers.

SUMMARY

Systems, methods, and/or processes for agricultural parameter determination are provided. One or more can include or use processing circuitry configured to: manage one or more land parcels for agricultural parameter optimization (such as soil organic carbon optimization); and determine one or more agricultural parameters of at least a portion of one land parcel (such as an alleyway between commodity crops) during a first time period without sampling the portion of the one land parcel.

The determining can include: collecting target agricultural data from collection sites of at least one portion of one land parcel, the collecting comprising compiling target agricultural data and candidate predictor parcel data associated with the collection sites; associating a subset of the candidate predictor parcel data with points throughout the portions of the land parcels; and processing both the compiled target agricultural data and the subset of predictive parcel data to generate target agricultural data for unsampled parcel portions of the one land parcel or of another, unsampled, land parcel.

The processing can include: building a target agricultural data model from collected target agricultural data and the subsets of predictive data; and applying the target agricultural data model to determine one or more agricultural parameters of a portion of the at least one land parcel.

Systems, methods, and/or processes for increasing soil organic carbon are also provided. They can include: managing a parcel having commodity crops planted in rows, the commodity crops having a dormant season; defining alleyways between the rows of commodity crops; planting a cover crop within the alleyways, the cover crops having a growing season within the dormant season of the commodity crop, and a dormant season within the growing season of the commodity crop; and increasing the soil organic carbon content of the parcel during the dormant season of the commodity crop with the cover crop.

Systems, methods, and/or processes for increasing soil organic carbon within land parcels having commodity crops separated by alleyways are also provided. They can include processing circuitry configured to: manage one or more land parcels for agricultural parameter optimization, the land parcels having commodity crops in rows with alleyways therebetween; and determine carbon per acre of at least one land parcel during a first time period without sampling the one land parcel.

The determining can include: collecting soil organic carbon data from collection sites of alleyways of another land parcel, the collecting can include: determining collection sites and compiling soil organic carbon data from the collection sites; associating a subset of the candidate predictor parcel data with points throughout the alleyways of the land parcels; and processing both the compiled soil organic data and the subset of predictive parcel data to generate soil organic data for unsampled alleyways. The processing can include: building a soil organic carbon data model from collected soil organic data and the subsets of predictive data; and applying the soil organic data model to determine soil organic carbon of the alleyway of the at least one land parcel.

DRAWINGS

Embodiments of the disclosure are described below with reference to the following accompanying drawings.

FIG. 1 is a depiction of system process parameters according to an embodiment of the disclosure.

FIG. 2 is another depiction of system process parameters according to an embodiment of the disclosure.

FIG. 3 is another depiction of system process parameters according to an embodiment of the disclosure.

FIG. 4 is another depiction of system process parameters according to an embodiment of the disclosure.

FIGS. 5A, 5B and 5C depict system and/or methods steps for determining sampling sites.

FIG. 6 is a depiction of example parcels and process parameters according to an embodiment of the disclosure.

FIG. 7 is another depiction of parcel portion digitization and processing according to an embodiment of the disclosure.

FIG. 8 is a depiction of digitized parcel portions according to an embodiment of the disclosure.

FIG. 9 is another depiction of system process parameters according to an embodiment of the disclosure.

FIG. 10 is another depiction of system process parameters according to an embodiment of the disclosure.

FIGS. 11 and 12 are examples of collection sites of target data collection in 12, and a map of predicted points for the entire area of alleyways in 13 according to an embodiment of the disclosure.

FIG. 13 is an example of sampled parcels with predicted sites and an un sampled parcel with predicted sites.

FIG. 14 is a depiction of Hybridized bulbosa cover crop grown in alleyways between commodity crops.

FIG. 15 is another depiction of Hybridized bulbosa cover crop grown in alleyways between commodity crops.

FIG. 16 is a depiction of the Hybridized bulbosa growth cycle within the alleyways in comparison to the growth cycle of a commodity crop (wine grapes).

FIG. 17 is a processing circuitry system according to an embodiment of the present disclosure.

DESCRIPTION

This disclosure is submitted in furtherance of the constitutional purposes of the U.S. Patent Laws “to promote the progress of science and useful arts” (Article 1, Section 8).

The present disclosure will be described with reference to FIGS. 1-17. Referring first to FIG. 1, an example method 10 to be performed by processing circuitry is provided. Method 10 begins with identifying an area 12. This identification can be a parcel, a portion of a parcel, a farm, and/or a plot, etc. Any form of an area for agricultural parameter determination can be identified. Multiple land parcels and/or multiple portions of multiple land parcels can be identified.

The method includes an intervention 14 into the area. This intervention can be the preparation of any kind of agricultural treatment, but in particular embodiments, environmentally positive agricultural treatments, such as but not limited to, the use of cover crops as will be detailed later by example.

The method can continue by defining initial condition 16. This can include collecting target agricultural data for at least one other land parcel, the collecting comprising determining sampling sites and compiling target agricultural data associated with the sampled sites. As an example, using soil data from publicly available, soil databases (e.g., gNATSGO), vegetation indices from drone imagery, and elevation data from public datasets can be processed by aggregating data and deriving the number of statistically different units or zones present within the parcel or parcel portion and then deriving where to sample within these units in order to best approximate the unit's statistical distribution. At least one example of this is depicted and described with reference to FIGS. 5A-5C.

The method continues with data collection of target variables 18. This can include the collection of target agricultural data for at least one portion of another land parcel or portion. This can be a collection of Soil Organic Carbon from an alleyway, for example. The collecting can include determining the collection sites and compiling target agricultural data associated with the samples acquired from the collection sites. This target agricultural data can include Total Carbon, Total Organic Carbon, Soil Organic Carbon, Calcium, Magnesium, and/or Nitrogen.

The method continues with collecting predictor variables based on locations of target variables 20. For example, candidate predictor parcel data can be compiled and associated with the collected target agricultural data samples acquired from the collection sites.

Predictor variables can include but are not limited to predictors derived from drone imagery and summarized over space across statistical metrics including but not limited to minimum, mean, maximum, standard deviation of various spectral indices including but not limited to CI, MCARI, NDWI, VARI, kNDVI, NDVI, SAVI, GNDVI, ENDVI, LCI, EVI, NIRv, GLI, CVI, CI RedEdge, and NDRE; predictors derived from various satellite imagery (e.g., ESA Copernicus Sentinel-1, ESA Copernicus Sentinel-2, PlanetScope, MODIS, etc) and summarized over space and time across statistical metrics including but not limited to minimum, mean, maximum, standard deviation of various spectral indices including but not limited to SCI, CI, NDWI, VARI, kNDVI, SAVI, ENDVI, LCI: B5, B6, B7; NIRv; GLI, CVI, CI RedEdge: B5, B6, B7; NDRE: B5, B6, B7; 3BSI, mND, MCARI, IRECI, NDVI, S2REP, SR, GNDVI, MDVI, MSI, EVI; predictors derived from sunlight hours at the location between the start of the growing season identified from the first time of rain and the drone flight date: average sunlight hours, total sunlight hours; predictors derived from daily meteorological variables and summarized over time across statistical metrics including but not limited to minimum, mean, maximum, standard deviation) of five variables (precipitation, shortwave radiation, maximum temperature, minimum temperature, vapor pressure); predictors derived from location information: system (e.g., “vineyard”, “orchard”), age, fertilizer rate, pure live seed goal, year, predictors derived from gNATSGO: taxorder, taxsuborder, taxgrtgroup, taxsubgrp, taxpartsize, ksat_r, awc_r, dbovendry_r, wthirdbar_r, wfifteenbar_r, kwfact, kffact, claytotal_r, om_r, musym; predictors derived from 12 SoilGrids250m: wrb, ocs, bdod, cec, cfvo, clay, nitrogen, phh2o, sand, silt, soc, ocd across four quantiles, Q0.05, Q0.5, mean and Q0.95, and 6 various depths: 0-5, 5-15, 15-30, 30-60, 60-100, and 100-200 cm (ocs is only distributed at depth 0-30 cm); and/or predictors derived from 13 Polaris (30-m resolution) variables: ph, om, clay, sand, silt, bd, hb, n, alpha, ksat, lambda, theta_r, theta_s across 5 quantiles (mean, mode, p50, p5, p95) at 3 depths (0-5, 5-15, 15-30 cm). Statistical metrics for summarizing drone imagery, satellite imagery and daily weather data can be automatically determined by the processing circuitry.

The method continues with processing and modeling these variables including machine learning 22. Accordingly, both the compiled target agricultural data and the predictive parcel data are processed to generate target agricultural data for unsampled parcel portions. The processing can include selecting a predictive parcel data subset from the predictive parcel data. The selection of the subset favors a parsimonious model that accomplishes the desired level of explanation or prediction with as few predictor variables as possible. The goodness of fit of a statistical model describes how well it fits a set of observations. The processing can include building a target agricultural data model using the sample data acquired at the collection sites and the subset of predictive data, and applying the target agricultural data model to determine one or more agricultural parameters of the unsampled land parcel.

The processing can include determining additional sample sites for the one land parcel and additional sample sites for the other land parcel. This can include selecting unsampled sites for target variable prediction.

The predictive parcel data subset can be associated with portions of the sampled and/or unsampled parcels. For example sampled alleyways and/or unsampled alleyways. Accordingly, the subset of predictive parcel data can be associated with a myriad of points throughout the parcel or parcel portion. The model can be derived using the subset of predictive data and the sampled data or the entirety of the predictive data and the sampled data.

The model can then be applied to the target agricultural data associated with the collection sites and/or unsampled land parcels or portions to determine the one or more agricultural parameters of the at least one land parcel.

As shown the method can continue with additional predictions including but not limited to annual predictions of target variables 24, and then a calculation of annual change 26, and finally quantifying the environmental benefits 28.

FIGS. 2 and 3 are examples of alternative embodiments of method 10 described in FIG. 1. This can include assessing environmental impact, providing a cover cropping system that indicates secondary changes and also primary change mechanisms, and then using the machine learning and/or process and model software to determine a net environmental impact and then monetizing the net environmental impact. FIG. 3 can include determining changes to a carbon footprint utilizing a specific plant variety such as hybridized bulbosa to increase soil carbon content by utilizing the machine learning and/or process and modeling software to determine annual change in soil carbon as a value proposition based on grower preference.

Referring next to FIG. 4, in a specific embodiment, method 40 can include identifying alleyways between permanent crops 42. This can include drone image segmentation as described with reference to FIGS. 8-9. In accordance with example implementations, portions of parcels, or alleyways between commodity crops are identified. Cover crops are planted within the alleyways 44. Initial soil organic carbon content can be determined from samples collected from sampling sites and analyzed at 46 and 48. These sampling sites can be determined in accordance with FIGS. 5A-5C. At 50, field samples collected and analyzed can be associated and/or connected to data (such as predictive data) related to intervention characteristics, annual high-resolution snapshots, growing season environmental conditions and permanent environmental conditions, for example. At 52 process and modeling that can include machine learning to predict the soil organic carbon content at 54. At 56, the annual change in soil organic carbon content can be calculated. At 58 carbon credits from additional soil organic carbon added to the parcel or portion thereof can be determined.

Referring next to FIGS. 5A-5C as well as Table 1 below, sampling site determination is described. To determine sampling sites in accordance with steps 1-3, soil information, elevation, and imagery are acquired from the parcels of interest. A statistical clustering identifies the number of statistically different groups of patterns and groups areas of the field into units and/or sampling zones. Processes for determining sample sites can also include those disclosed in Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics), 28(1), 100-108, and/or Pelleg, D., & Moore, A. W. (2000, June). X-means: Extending k-means with efficient estimation of the number of clusters. In IcmI (Vol. 1, pp. 727-734), the entirety of both of which are incorporated by reference herein.

	TABLE 1
	Points	Feature 1	Feature 2	Feature 3	Feature 4

	P1	100	45	0.1	1000
	P2	99	50	0.1	900
	P3	14	2	9	25
	P4	75	55	0.1	800
	P5	15	2	10	30
	P6	13	1	11	34
	P7	25	8	0.1	0
	P8	29	10	0.01	2
	P9	31	10.5	0.1	1

In accordance with the below reference, a second statistical algorithm is run for each zone that identifies where within the sampling zone to best represent the statistical distribution of the sampling zones. Processes for determining sample sites can also include those disclosed in Minasny, B., & McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & geosciences, 32(9), 1378-1388 the entirety of which is incorporated by reference herein.

In italics of Table 1 are group three data corresponding to group 3(P7-P9) of FIG. 5B. These features are shown in the 5B graph. This determination can be made for sampling target data.

The processing circuitry of the system of the present disclosure is not limited to that depicted in FIG. 17. For example, the processing circuitry can include a personal computing system that includes a computer processing unit that can include one or more microprocessors, one or more support circuits, circuits that include power supplies, clocks, input/output interfaces, circuitry, and the like. Generally, all computer processing units described herein can be of the same general type. Application Programming Interface (API) can allow for communication between different software applications in the system. The memory can include random access memory, read-only memory, removable disc memory, flash memory, and various combinations of these types of memory. The memory can be referred to as a main memory and be part of a cache memory or buffer memory. The memory can store various software packages and components such as an operating system.

The computing system may also include a web server that can be of any type of computing device adapted to distribute data and process data requests. The web server can be configured to execute system application software such as the reminder schedule software, databases, electronic mail, and the like. The memory of the web server can include system application interfaces for interacting with users and one or more third party applications. Computer systems of the present disclosure can be standalone or work in combination with other servers and other computer systems that can be utilized, for example, with larger corporate systems such as financial institutions, insurance providers, and/or software support providers. The system is not limited to a specific operating system but may be adapted to run on multiple operating systems such as, for example, Linux and/or Microsoft Windows. The computing system can be coupled to a server and this server can be located on the same site as the computer system or at a remote location, for example.

In accordance with example implementations, these processes may be utilized in connection with the processing circuitry described. The processes may use software and/or hardware of the following combinations or types. For example, with respect to server-side languages, the circuitry may use Java, Python, PHP, .NET, Ruby, JavaScript, Golang, R, or Dart, for example. Some other types of servers that the systems may use include Apache/PHP, .NET, Ruby, NodeJS, Java, R, Golang, and/or Python. Databases that may be utilized are Oracle, MySQL, SQL, NoSQL, or SQLite (for Mobile) as well as any type of relational, key-value, in memory, document, wide column, graph, time series, or ledger databases. Client-side languages that may be used, this would be the user side languages, for example, are ASM, C, C++, C#, Java, Objective-C, Swift, ActionScript/Adobe AIR, or JavaScript/HTML5. Communications between the server and client may be utilized using TCP/UDP Socket based connections, for example, as Third-Party data network services that may be used include GSM, LTE, HSPA, UMTS, CDMA, WiMAX, WIFI, Cable, and DSL. The hardware platforms that may be utilized within processing circuitry include embedded systems such as (Raspberry PI/Arduino), (Android, iOS, Windows Mobile), phones and/or tablets, or any embedded system using these operating systems, i.e., cars, watches, glasses, headphones, augmented reality wear, etc., or desktops/laptops/hybrids (Mac, Windows, Linux). The architectures that may be utilized for software and hardware interfaces include x86 (including x86-64), or ARM.

The systems and/or processing circuitry of the present disclosure can include a server or cluster of servers, one or more devices, additional computing devices, several network connections linking devices to server(s) including the network connections, one or more databases, and a network connection between the server and the additional computing devices, such as those devices that may be linked to an adjuster.

Devices and/or processing circuitry and/or plurality of devices and the additional computing device can be any type of communication devices that support network communication, including a telephone, a mobile phone, a smart phone, a personal computer, a laptop computer, a smart watch, a personal digital assistant (PDA), a wearable or embedded digital device(s), a network-connected vehicle, etc. In some embodiments, the devices and the computing device can support multiple types of networks. For example, the devices and the computing device may have wired or wireless network connectivity using IP (Internet Protocol) or may have mobile network connectivity allowing over cellular and data networks.

The various networks may take the form of multiple network topologies. For example, networks can include wireless and/or wired networks. Networks can link the server and the devices. Networks can include infrastructure that support the links necessary for data communication between at least one device and a server. Networks may include a cell tower, base station, and switching network as well as cloud-based networks.

Referring next to FIG. 6, the modeling domain 70 (which can include a parcel or portion of a parcel) is shown, which encompasses rectangles that denote data. At 100, target agricultural data is collected for the purpose of expanding the modeling domain. This target agricultural data is acquired from samples taken from collection sites. At 102, using the target agricultural data and predictive parcel data, modeled target agricultural data is determined. At 104, target agricultural data is collected to improve model performance. This target agricultural data is sampled at collection sites that were either previously sampled or previously modeled. Accordingly, at t1, two samples 100 were taken and modeled data is determined at 102. This process can be iterative as shown by example, with initial conditions (tn+1). Accordingly, domain 80 is shown that may encompass domain 70 at t2, while domain 90 is shown that may encompass both domains 80 and 70 at t3. As shown, the sampling and modeling is different at each time step t1-t3. At t2, domain 80 has target agricultural data 110 (110 rather 100 because this data is collected at t2) collected. This data is processed with predictive parcel data to determine modeled data 112. Additionally, previously sampled 100 at t1 is sampled for performance at 114.

At t3, domain 90 has target agricultural data 120 collected to expand modeling domain. This data a is processed with predictive parcel data to determine modeled data 122. Additionally, at t3, sample for performance is taken at 124. At each iteration the target agricultural model is updated. Referring next to FIG. 7, an example classification of images for determining the alleyways between commodity crops is shown. The alleyways and the borders are defined throughout the imagery to dictate where overlap occurs between alleyways and commodity crops using processing circuitry. For example, a myriad patches can be defined and these patches can be training data for a model. Accordingly, parcels can be provided as shown in FIG. 8, wherein black indicates the alleyways and white indicates the commodity crops. In accordance with example implementations, the masked images can be used to determine sampling sites. For example, sampling sites for actual sampling and/or prediction can be confined to sites within the alleyways.

Referring to FIG. 9, an overall system and/or method is shown. In accordance with example implementations, the system inputs or predictive data can include both internal data and/or external data. The internal data is data acquired by the user, for example drone imagery and/or location metadata (for example, agricultural system, type of crop, age of intervention, etc.). The external data is typically publicly available data. For example, weather data time series and/or multiple satellite imagery timeseries. As shown, this data can be preprocessed which includes summarizing vegetation indices tables which can then be used as training data. The training data can be used to train a machine learning model, and the model can be used to generate modeled data as an output. In accordance with example implementations and with reference to FIG. 10, the system provides for a benchmarking operation and a validation operation as well as a tuning operation and a training operation and then a testing operation of the system as shown to validate the system.

Referring next to FIGS. 11 and 12, a parcel 200 has been identified in FIG. 11, and in FIG. 12 a digitized parcel 202 is shown that demonstrates modeled data. Sample sites have been determined within parcel 200 and these sample sites are within alleyways between commodity crops. Referring first to Tables 2 and 3, point measurements may be acquired at points 1, 2 and 3 as shown in FIG. 12.

TABLE 2
Point Measurements of Parcel 200

	Point 1:	6	tons C/acre
	Point 2:	15	tons C/acre
	Point 3:	13	tons C/acre
	Average:	11	tons C/acre
	Standard Deviation:	3	tons C/acre
	Annual Change 2021-2022:	0.2	tons C/acre

	Model Performance:	20% MAPE

TABLE 3
Modeled Data of Parcel 202

	Average	11	tons C/acre
	Standard Deviation	3	tons C/acre
	Annual Change 2021-2022	+0.2	tons C/acre

	Model Performance:	20% MAPE

As shown in FIG. 14, at target data of parcel 190 there are sampling points A, B, C and D within alleyways that have been provided through masking as described. In parcel 200 sampling points F and E are shown, and in parcel 300, no actual sampling has been performed. Accordingly, FIG. 14 is an example use of at least two parcels (190 and 200) and portions of each to determine carbon per acre of parcel or portions of parcel 300. Data associated with parcel 190 is represented in the tables as X2, data associated with parcel 200 is represented in the tables at V4, and data associated with parcel 300 is represented in the tables as V3. As a legend, 180 can represent a parcel having a boundary, 186 can represent the subset of predictive data associated with modeled parcels or portions of Step 2, and 188 can represent validated, or modeled values compared to observed values at Step 3.

In accordance with example implementations, to tables 4-9, the processing circuitry utilizing publicly available machine learning programs such as any ensemble or combination of a time series analysis, a spectral analysis, a network analysis, a correlation analysis, a generalized linear model, a generalized additive model, a nearest-neighbor model, a decision tree model, a support vector machine model, a Bayesian model, a Gaussian processes model, an artificial neural network, a recurrent neural network, a convolutional neural network, a generative adversarial neural network, a transformer model, and with operations such as a clustering operation, a regression operation, a classification operation, a feature selection operation, a feature engineering operation, a spatio-temporal resampling operation, a benchmarking operation, a training operation, a testing operation, a validation operation, a tuning operation, a genetic algorithm operation, a Monte-Carlo operation, a bagging operation, an ensembling operation, a gradient boosting operation, a regularization operation, a supervised learning operation, an unsupervised learning operation, a self-supervised learning operation, a semi-supervised learning operation, a reinforcement learning operation, and a sequence-to-sequence learning operation

As an example and with reference to Table 4 multiple samples were taken at collection sites of parcels 190 and 200.

TABLE 4
Target Agricultural Data Sampled

					depth	fresh weight	dry weight	toc
Parcel	date collected	description	type	zone	(cm)	(g)	(g)	%
V4	Apr. 6, 2021	V4-P-4	soil		30	54.21
V4	Apr. 6, 2021	V4-P-5	soil		30	113.56
V4	Apr. 6, 2021	V4-P-10	soil		30	92.388
V4	Apr. 6, 2021	V4-P-1SX	soil sub-		30	86.019
			composite
V4	Apr. 6, 2021	V4-P-2SX	soil sub-		30	86.053
			composite
V4	Apr. 6, 2021	V4-P-3SX	soil sub-		30	85.297
			composite
V4	Mar. 4, 2022	V4-P-2	soil		30	41.502
V4	Mar. 4, 2022	V4-P-10	soil		30	34.406
V4	Mar. 4, 2022	V4-P-5	soil		30	36.991
V4	Mar. 4, 2022	V4-P-7	soil		30	47.111
V4	Mar. 4, 2022	V4-P-4	soil		30	50.01
V4	Mar. 4, 2022	V4-P-1SX	soil sub-		30	52.513	48.962
			composite
V4	Mar. 4, 2022	V4-P-2SX	soil sub-		30	52.503	46.818
			composite
V4	Mar. 4, 2022	V4-P-3SX	soil sub-		30	52.511	49.896
			composite
V4	Mar. 4, 2022	V4-P-4SX	soil sub-		30	51.747	47.36	0.62
			composite
X2	Mar. 20, 2021	X2-P-1	soil		30	103.013
X2	Mar. 20, 2021	X2-P-2	soil		30	93.779
X2	Mar. 20, 2021	X2-P-3	soil		30	122.414
X2	Mar. 20, 2021	X2-P-4	soil		30	104.443
X2	Mar. 20, 2021	X2-P-5	soil		30	88.387
X2	Mar. 20, 2021	X2-P-6	soil		30	91.791
X2	Mar. 20, 2021	X2-P-7	soil		30	87.666
X2	Mar. 20, 2021	X2-P-8	soil		30	87.053
X2	Mar. 20, 2021	X2-P-10	soil		30	79.573
X2	Mar. 20, 2021	X2-P-11	soil		30	97.584
X2	Mar. 20, 2021	X2-P-1SX	Soil Sub-		30	316.737
			composite
X2	Mar. 20, 2021	X2-P-2SX	Soil Sub-		30	315.583
			composite
X2	Mar. 20, 2021	X2-P-3SX	Soil Sub-		30	309.573
			composite
X2	Feb. 12, 2022	X2-P-4	soil		30	44.22
X2	Feb. 12, 2022	X2-P-9	soil		30	41.32
X2	Feb. 12, 2022	X2-P-10	soil		30	52.366
X2	Feb. 12, 2022	X2-P-7	soil		30	43.364
X2	Feb. 12, 2022	X2-P-5	soil		30	50.356
X2	Feb. 12, 2022	X2-P-1SX	soil sub-		30	57.926	46.77
			composite
X2	Feb. 12, 2022	X2-P-2SX	soil sub-		30	57.943	46.891
			composite
X2	Feb. 12, 2022	X2-P-3SX	soil sub-		30	57.945	47.508
			composite
X2	Feb. 12, 2022	X2-P-4SX	soil sub-		30	56.736	44.67	1.7
			composite
X2	Feb. 12, 2022	X2-P-4SX	soil sub-					1.68
			composite
V4	Jul. 19, 2021	V4-P-0BD	bulk density	0
V4	Jul. 19, 2021	V4-P-4BD	bulk density	4
V4	Jul. 19, 2021	V4-P-1BD	bulk density	1
V4	Jul. 19, 2021	V4-P-2BD	bulk density	2

		bd		Amount	(mg)		(mg)
	Parcel	(g/cm3)	bd_p50_15_30	(mg)	N	% N	C	% C	C:N Ratio
	V4
	V4
	V4
	V4			19.5	0.017	0.09	0.184	0.94	10.8235294
	V4			15.6	0.012	0.08	0.123	0.79	10.25
	V4			16.6	0.013	0.08	0.135	0.81	10.3846154
	V4		1.53
	V4		1.27
	V4		1.63
	V4		1.63
	V4		1.29
	V4			16.6	0.017	0.1	0.156	0.96	9.18
	V4			18.4	0.017	0.1	0.171	1.01	10.06
	V4			17.1	0.017	0.1	0.154	0.93	9.06
	V4
	X2
	X2
	X2
	X2
	X2
	X2
	X2
	X2
	X2
	X2
	X2			19.9	0.031	0.16	0.312	1.57	10.0645161
	X2			17.5	0.024	0.13	0.292	1.67	12.1666667
	X2			17.9	0.02	0.11	0.26	1.45	13
	X2		1.39
	X2		1.39
	X2		1.39
	X2		1.49
	X2		1.49
	X2			17.8	0.02	0.11	0.392	2.2	19.6
	X2			16	0.015	0.1	0.328	2.05	21.87
	X2			19.2	0.019	0.1	0.41	2.14	21.58
	X2
	X2
	V4	1.44
	V4	0.57
	V4	1.30
	V4	1.43

In accordance with example implementations, the target data of Table 4 samples were associated with a myriad of candidate predictor data associated with the collection site at 183, for example. The sample data is shown associated with candidate predictor data in Table 5.

These 68 predictor values as shown in Table 5 are summarized over space across four summary metrics. There are also 128 predictors derived from Sentinel-2 imagery summarized over time and then two predictors derived from sunlight hours, 20 predictors derived from daily meteorological summarized over time, five predictors derived from location information and 15 predictors derived from gNATSGO as well as 245 predictors derived from soil grids 250 variables. Predictors can also include legacy predictors, two of which are derived from clay content as well.

TABLE 5
Candidate Predictor Values

	Parcel X2
Predictive	Sample Location

Parcel Data	X2-P-4	X2-P-9	X2-P-10	X2-P-7	X2-P-5
CI_min	0.179167718	0.210017985	0.230493887	0.307210737	0.120695699
CI_mean	0.490652738	0.52563623	0.455869832	0.519407918	0.485004001
CI_max	0.617626555	0.634187752	0.586850959	0.639792967	0.633414048
CI_sd	0.084537601	0.088728514	0.056066238	0.058753236	0.10596902
MCARI_min	7.14E−06	−9.45E−07	−4.27E−06	−4.18E−06	−3.14E−06
MCARI_mean	0.00580061	0.003927367	0.005706154	0.005717895	0.004352375
MCARI_max	0.020021112	0.023502497	0.013211475	0.022790876	0.020354753
MCARI_sd	0.004298485	0.004404259	0.005158668	0.004683299	0.004301355
NDWI_min	−0.822735055	−0.821158896	−0.863967714	−0.817751488	−0.856799933
NDWI_mean	−0.699771811	−0.677040129	−0.680822088	−0.702496749	−0.660539745
NDWI_max	−0.424344443	−0.40932772	−0.396501297	−0.398227378	−0.414774299
NDWI_sd	0.099306333	0.08435168	0.139520185	0.1106884	0.122194629
VARI_min	−0.292241596	−0.350405477	−0.351743352	−0.322972416	−0.317569917
VARI_mean	−0.017370633	−0.154496898	−0.047931958	−0.036073158	−0.079548942
VARI_max	0.525553297	0.442091979	0.421903386	0.410072665	0.584522547
VARI_sd	0.2070003	0.166573936	0.210401291	0.177343437	0.224827227
kNDVI_min	0.000224572	0.075771581	0.05669348	0.069713954	0.073722392
kNDVI_mean	0.433830582	0.34923593	0.402701833	0.433372216	0.364011409
kNDVI_max	0.681886457	0.666383155	0.670071754	0.647479951	0.708404553
kNDVI_sd	0.165648632	0.141959028	0.22143015	0.181466614	0.191967817
NDVI_min	0.283544261	0.275530566	0.258442701	0.26424841	0.27176532
NDVI_mean	0.673483126	0.596077507	0.63526929	0.67074815	0.602519511
NDVI_max	0.912486427	0.8967769	0.900485068	0.878036047	0.940198878
NDVI_sd	0.167226453	0.141099257	0.225146433	0.181953419	0.196078124
SAVI_min	0.083607638	0.065354536	0.04620825	0.068617517	0.049476246
SAVI_mean	0.441743859	0.381067537	0.403961314	0.435083387	0.384575409
SAVI_max	0.718017193	0.730439052	0.703324349	0.68561899	0.836735249
SAVI_sd	0.150623331	0.134185255	0.214069315	0.169131679	0.188756418
GNDVI_min	0.424344443	0.40932772	0.398501297	0.398227378	0.414774299
GNDVI_mean	0.699771811	0.677040129	0.680822088	0.702496749	0.660539746
GNDVI_max	0.822735056	0.821158896	0.869957714	0.817751488	0.855799933
GNDVI_sd	0.099306333	0.08435166	0.139520186	0.1106884	0.122194629
ENDVI_min	0.356668754	0.301090749	0.25722593	0.316422	0.2769425
ENDVI_mean	0.721730561	0.674272586	0.670575466	0.725823789	0.661852343
ENDVI_max	0.88668654	0.861690575	0.904745045	0.87786623	0.906204897
ENDVI_sd	0.136804679	0.115459944	0.194289039	0.154531102	0.175403556
LCI_min	0.205417711	0.209129882	0.1880328	0.202739503	0.191494792
LCI_mean	0.480490339	0.433581495	0.464080978	0.48421181	0.445456498

		Parcel V4
	Predictive	Sample Location

	Parcel Data	V4-P-2	V4-P-10	V4-P-5	V4-P-7
	CI_min	0.078302272	0.111900615	0.038698810	0.205234402
	CI_mean	0.350495441	0.394993664	0.363795543	0.484769094
	CI_max	0.591839445	0.62611427	0.519228247	0.516806085
	CI_sd	0.094555841	0.108786541	0.084123505	0.079807487
	MCARI_min	5.21E−05	3.14E−05	8.74E−05	1.58E−05
	MCARI_mean	0.003980679	0.002557302	0.004058522	0.00135785
	MCARI_max	0.01624406	0.011076091	0.012608365	0.008720223
	MCARI_sd	0.003688326	0.003045479	0.002618125	0.001818403
	NDWI_min	−0.796137507	−0.738301297	−0.771056917	−0.701062675
	NDWI_mean	−0.577147701	−0.492222958	−0.605422885	−0.45388792
	NDWI_max	−0.306498978	−0.308084077	−0.327505917	−0.313362699
	NDWI_sd	0.155216881	0.149706584	0.11825602	0.113707597
	VARI_min	−0.247734513	−0.301510019	−0.223557744	−0.277955424
	VARI_mean	0.101994413	−0.017488034	0.096428283	−0.139754142
	VARI_max	0.666542987	0.428531559	0.581959793	0.345127085
	VARI_sd	0.236862536	0.225182255	0.19220215	0.13950943
	kNDVI_min	0.018748829	0.014045902	0.044286027	0.015440634
	kNDVI_mean	0.366601457	0.247858525	0.391063587	0.156062253
	kNDVI_max	0.664266383	0.61954207	0.662423225	0.566085925
	kNDVI_sd	0.236752513	0.221050807	0.181620856	0.155124515
	NDVI_min	0.136934389	0.118519304	0.210511315	0.124265284
	NDVI_mean	0.587343945	0.451562822	0.626017184	0.356517309
	NDVI_max	0.894555821	0.851035578	0.892815385	0.801089325
	NDVI_sd	0.258147142	0.257355507	0.193492148	0.190835874
	SAVI_min	0.064394546	0.075575543	0.061178497	0.076653534
	SAVI_mean	0.313066551	0.238582358	0.33311175	0.178107177
	SAVI_max	0.636013607	0.544438092	0.572615732	0.472104045
	SAVI_sd	0.177276393	0.15364951	0.124576985	0.096225335
	GNDVI_min	0.306498978	0.308084077	0.327505917	0.313362699
	GNDVI_mean	0.577147701	0.492222958	0.605422885	0.45388792
	GNDVI_max	0.796137507	0.738301297	0.771056917	0.701052676
	GNDVI_sd	0.165216881	0.149708584	0.11825602	0.113707597
	ENDVI_min	0.286414958	0.304890713	0.312452726	0.369076621
	ENDVI_mean	0.810624213	0.522339819	0.637716277	0.491455071
	ENDVI_max	0.8553800	0.807328739	0.833448071	0.753179397
	ENDVI_sd	0.191950326	0.164952039	0.143876173	0.10340289
	LCI_min	0.085025621	0.067848885	0.137335326	0.079849931
	LCI_mean	0.375462161	0.280871784	0.391787803	0.210587874

	Parcel X2
	Sample Location

	X2-P-4	X2-P-9	X2-P-10	X2-P-7	X2-P-5
LCI_max	0.651713205	0.638710557	0.689423053	0.640112548	0.734597912
LCI_sd	0.104306524	0.085491496	0.141010307	0.115384016	0.129366623
EVI_min	0.075538843	0.058924869	0.041296473	0.061397705	0.044130583
EVI_mean	0.452193526	0.378851921	0.420486428	0.443830854	0.391327515
EVI_max	0.817297988	0.856816513	0.791846761	0.77336168	1.008477412
EVI_sd	0.172539403	0.156166382	0.239556519	0.187879957	0.214475538
NIRv_min	0.069064699	0.049934309	0.0382699	0.059407242	0.036641385
NIRv_mean	0.322603278	0.294646427	0.293813893	0.31384686	0.291477404
NIRv_max	0.554616809	0.595755544	0.559029996	0.531940043	0.715683937
NIRv_sd	0.09838997	0.092854499	0.143492036	0.110174591	0.130478001
GLI_min	−0.07161784	−0.092493087	−0.108026451	−0.082469222	−0.082780604
GLI_mean	0.128761802	0.036765157	0.099584751	0.125333076	0.084268651
GLI_max	0.462951189	0.374599567	0.395188971	0.405554156	0.472740686
GLI_sd	0.132800946	0.102399262	0.144793209	0.12395267	0.145848808
CVI_min	3.080762759	2.884845234	2.943879241	3.141879651	3.151760106
CVI_mean	6.173023851	6.88232553	6.293305121	6.541599011	5.974441352
CVI_max	8.774352492	9.608594713	9.745779222	9.896908826	10.38196655
CVI_sd	1.325121771	1.258829949	1.593696337	1.578851171	1.524421008
CI_Rededge_min	0.454588724	0.456386859	0.411252782	0.460715416	0.401948491
CI_Rededge_mean	1.371252164	1.202502495	1.357342987	1.4115550801	1.30121101
CI_Rededge_max	2.190341837	2.078544158	2.59814453	2.21955144	3.120602488
CI_Rededge_sd	0.36438188	0.287537438	0.513335956	0.419919374	0.519323804
NDRE_min	0.185199549	0.185796003	0.170555849	0.18722824	0.167342677
NDRE_mean	0.399204615	0.370440608	0.389673286	0.403988808	0.379994611
NDRE_max	0.52271197	0.509640968	0.574300027	0.526015969	0.609420961
NDRE_sd	0.070512147	0.056797602	0.096401189	0.079663874	0.09159187
system	Vineyard	Vineyard	Vineyard	Vineyard	Vineyard
crop	Wine grapes	Wine grapes	Wine grapes	Wine grapes	Wine grapes
age	3	3	3	3	3
prcp_mean	3.583506927	3.583506927	3.583506927	3.583506927	3.583506927
prcp_max	138.7399979	138.7399979	138.7399979	138.7399979	138.7399979
prcp_sd	13.78393873	13.78393873	13.78393873	13.78393873	13.78393873
srad_min	57.93000031	57.93000031	57.93000031	57.93000031	57.93000031
srad_mean	243.1942705	243.1942705	243.1942705	243.1942705	243.1942705
srad_max	449.1699982	449.1699982	449.1699982	449.1699982	449.1699982
srad_sd	93.71569219	93.71569219	93.71569219	93.71569219	93.71569219
tmax_min	7.869999886	7.869999886	7.869999886	7.869999886	7.869999886
tmax_mean	18.82722225	18.82722225	18.82722225	18.82722225	18.82722225
tmax_max	34.875	34.875	34.875	34.875	34.875
tmax_sd	5.819062389	5.819062389	5.819062389	5.819062389	5.819062389
tmin_min	−0.925000012	−0.925000012	−0.925000012	−0.925000012	−0.925000012
tmin_mean	7.037604171	7.037604171	7.037604171	7.037604171	7.037604171
tmin_max	13.99499989	13.99499989	13.99499989	13.99499989	13.99499989
tmin_sd	3.540111509	3.540111509	3.540111509	3.540111509	3.540111509
vp_min	331.4100037	331.4100037	331.4100037	331.4100037	331.4100037
vp_mean	853.4975334	853.4975334	853.4975334	853.4975334	853.4975334
vp_max	1496.5	1496.5	1496.5	1496.5	1496.5
vp_sd	297.7363722	297.7363722	297.7363722	297.7363722	297.7363722
sunlight_hr_mean	10.35992146	10.35992146	10.35992146	10.35992146	10.35992146
sunlight_hr_sum	1502.188611	1502.188611	1502.188611	1502.188611	1502.188611
sampling_date.y	2022-02-10	2022-02-10	2022-02-10	2022-02-10	2022-02-10
SCI_S2_min	−0.103550296	−0.118054006	−0.114543115	−0.198720877	−0.109693262
SCI_S2_mean	−0.037066052	−0.051367774	−0.050919963	−0.08693389	−0.035724108
SCI_S2_max	0.04866426	0.055604075	0.053117783	0.016896209	0.041836581
SCI_S2_sd	0.034973128	0.043936348	0.046472457	0.066142846	0.034757288
CI_S2_min	0.095360825	0.079552926	0.079790713	0.074440053	0.054824561
CI_S2_mean	0.386956291	0.375101432	0.364571367	0.378394894	0.362377158
CI_S2_max	0.615321252	0.618559636	0.63317757	0.604669887	0.614107884
CI_S2_sd	0.163385594	0.168067587	0.169135545	0.163274104	0.172328609
NDWI_S2_min	−0.682749045	−0.682229965	−0.682038052	−0.693553223	−0.664552949
NDWI_S2_mean	−0.552189435	−0.557473272	−0.555963723	−0.559739718	−0.558000471
NDWI_S2_max	−0.343935382	−0.334446764	−0.336949718	−0.351855527	−0.333616299
NDWI_S2_sd	0.095852876	0.104511979	0.103932312	0.09843271	0.100702988
VARI_S2_min	−0.277821626	−0.292328042	−0.30589949	−0.286769231	−0.280395137
VARI_S2_mean	−0.053989481	−0.044981309	−0.035130452	−0.010403542	−0.030977465
VARI_S2_max	0.191458027	0.218002813	0.226804124	0.282608696	0.207578254
VARI_S2_sd	0.157428578	0.178443792	0.183894386	0.203488832	0.173741588
kNDVI_S2_min	0.094301978	0.084381181	0.091387994	0.108715388	0.092647119
kNDVI_S2_mean	0.271012386	0.290128152	0.29220169	0.295706431	0.287142436
kNDVI_S2_max	0.45339859	0.480126608	0.485929732	0.513705976	0.472089578
kNDVI_S2_sd	0.120436596	0.142907301	0.146216716	0.160381689	0.135023714
SAVI_S2_min	0.46127067	0.436202656	0.454046101	0.495514658	0.457178449
SAVI_S2_mean	0.775309563	0.8003171	0.803534309	0.806316368	0.797913841
SAVI_S2_max	1.048712108	1.084764456	1.092583845	1.130063635	1.073928043
SAVI_S2_sd	0.189460296	0.222056506	0.224048965	0.244060059	0.209285775
ENDVI_S2_min	0.262233375	0.258271352	0.259898477	0.278177155	0.252835896
ENDVI_S2_mean	0.54852551	0.557059062	0.555231182	0.567183752	0.549572976
ENDVI_S2_max	0.674635889	0.705352411	0.715039678	0.752291305	0.739558025
ENDVI_S2_sd	0.131876851	0.144470880	0.140396643	0.142680403	0.140826838
LCI_S2_B5_min	0.219729207	0.207595435	0.209720237	0.221722003	0.215559767
LCI_S2_B5_mean	0.383871068	0.40136283	0.40100707	0.397586891	0.399005757
LCI_S2_B5_max	0.551599965	0.557831705	0.565984252	0.574299462	0.549786395
LCI_S2_B5_sd	0.094630161	0.105950743	0.111130124	0.122758054	0.102911713
LCI_S2_B6_min	0.085188028	0.077628793	0.087135506	0.04585759	0.098006645
LCI_S2_B6_mean	0.148595756	0.147990556	0.147638259	0.154914201	0.176618545
LCI_S2_B6_max	0.199522673	0.199661591	0.190462754	0.205725735	0.243587835
LCI_S2_B6_sd	0.034869238	0.036775408	0.032067043	0.035428107	0.039175709
LCI_S2_B7_min	0.029728725	0.014467184	0.000343053	−0.016010385	0.057931834
LCI_S2_B7_mean	0.075469749	0.068649046	0.068035154	0.077692584	0.10489218
LCI_S2_B7_max	0.112649165	0.107875411	0.122282609	0.124823097	0.182478959
LCI_S2_B7_sd	0.025861716	0.02363296	0.02610168	0.032379523	0.027709092
NIRv_S2_min	1726	1636	1648	1710	1504
NIRv_S2_mean	2663.4375	2689.6875	2680.875	2954.40525	2572.71875
NIRv_S2_max	3618	3699	3625	3980	3553
NIRv_S2_sd	499.9369597	452.2335913	463.202151	477.0825221	484.8273543
GLI_S2_min	−0.037518038	−0.059606657	−0.065786332	−0.050445104	−0.53066412
GLI_S2_mean	0.067190315	0.069821128	0.073115803	0.095181918	0.076108053
GLI_S2_max	0.218262806	0.217391304	0.212477928	0.284512618	0.227790433
GLI_S2_sd	0.08387675	0.097097855	0.097482372	0.117666037	0.094517492
CVI_S2_min	2.182998571	2.153445559	2.148333354	2.145762029	2.133842347
CVI_S2_mean	4.044151796	4.189820699	4.108489159	3.851524008	3.974820041
CVI_S2_max	6.243507873	6.389281555	6.257888099	5.599971517	6.054186851
CVI_S2_sd	1.272033295	1.27375194	1.23572309	1.087907079	1.202721204
CI_Rededge_S2_B5_min	0.606238859	0.474157303	0.471750115	0.499788584	0.496903287
CI_Rededge_S2_B5_mean	1.057148316	1.33551377	1.136602378	1.125210264	1.122394666
CI_Rededge_S2_B5_max	1.851754706	1.836099585	1.897571278	1.923222749	1.78358209
CI_Rededge_S2_B5_sd	0.362102638	0.401274899	0.429984716	0.480669856	0.38829966
CI_Rededge_S2_B6_min	0.116842105	0.101289134	0.114054782	0.066084788	0.176779026
CI_Rededge_S2_B6_mean	0.250822098	0.248697784	0.245304715	0.256246738	0.303875165
CI_Rededge_S2_B6_max	0.387395737	0.383947939	0.383249882	0.364587876	0.441636582
CI_Rededge_S2_B6_sd	0.079051363	0.086776994	0.073289942	0.064640762	0.079858772
CI_Rededge_S2_B7_min	0.036463081	0.017439387	0.000403226	−0.021179164	0.095703125
CI_Rededge_S2_B7_mean	0.114357166	0.102194378	0.10142903	0.114959213	0.159440585
CI_Rededge_S2_B7_max	0.187153053	0.178019223	0.207026349	0.199769939	0.273823192
CI_Rededge_S2_B7_sd	0.045649636	0.041427663	0.043656177	0.048495055	0.043006152
NDRE_S2_B5_min	0.201991465	0.19164396	0.190656718	0.199932341	0.199007823
NDRE_S2_B5_mean	0.337189035	0.352043532	0.350897292	0.345851125	0.349876205
NDRE_S2_B5_max	0.480757483	0.478637101	0.48885993	0.490215028	0.471400394
NDRE_S2_B5_sd	0.075516724	0.082129244	0.086963584	0.095702133	0.00010748
NDRE_S2_B6_min	0.05519642	0.04820333	0.053950722	0.031985516	0.081211287
NDRE_S2_B6_mean	0.110380035	0.109321914	0.108334233	0.112848009	0.130879963
NDRE_S2_B6_max	0.162267081	0.161055505	0.153707775	0.154186647	0.180877279
NDRE_S2_B6_sd	0.031041316	0.034106721	0.029044875	0.025111375	0.030311953
NDRE_S2_B7_min	0.017905103	0.008644318	0.000201572	−0.010702922	0.045666355
NDRE_S2_B7_mean	0.053657485	0.048253335	0.047867182	0.053862518	0.07348117
NDRE_S2_B7_max	0.085569253	0.081734459	0.093803297	0.090814014	0.120424135
NDRE_S2_B7_sd	0.020492575	0.018853895	0.019859305	0.022192691	0.018304497
Three_BSI_Tian_S2_min	−0.777507303	−0.7771261	−0.774469124	−0.748275862	−0.768972142
Three_BSI_Tian_S2_mean	−0.696807874	−0.704142247	−0.70221834	−0.67206021	−0.692546212
Three_BSI_Tian_S2_max	−0.573115851	−0.575081226	−0.577008929	−0.049921255	−0.670040023
Three_BSI_Tian_S2_sd	0.055082574	0.057626315	0.057501905	0.049921255	0.056447329
mND_Verrelat_S2_max	258.7142857	121.4285714	552.3333333	1403	90
MCARI_S2_min	77325.2	36556	60672	68686.8	60669
MCARI_S2_mean	161161.3375	159155.7125	160680.35	217056.2063	147968.0563
MCARI_S2_max	237718	305393.6	295678	394941.6	274344
MCARI_S2_sd	46860.50939	84485.28133	69775.73444	103384.5848	66197.89647
IRECI_S2_min	1413.9125	1386.267757	1437.691318	1606.20122	1166.662347
IRECI_S2_mean	2600.366788	2806.600814	2819.022178	2998.916927	2323.473045
IRECI_S2_max	4010.256303	4795.910345	4811.117296	4957.816114	3944.224299
IRECI_S2_sd	861.9203899	1210.396217	1244.968369	1292.498893	864.8538764
NDMI_S2_min	−0.04866426	−0.055804076	−0.053117783	−0.016896209	−0.041836581
NDMI_S2_mean	0.037066052	0.051367774	0.050919963	0.08693389	0.035724106
NDMI_S2_max	0.103550296	0.118054006	0.114543115	0.198720877	0.109693252
NDMI_S2_sd	0.034973128	0.043936348	0.046472457	0.066142845	0.034757286
S2REP_min	712.0216049	713.891129	712.8521127	712.1296296	712.2585227
S2REP_mean	718.1787359	719.4609392	718.8287051	718.2368845	718.0607826
S2REP_max	722.5	725.5523256	723.2191781	721.7427885	722.862069
S2REP_sd	2.844674495	3.729494589	2.821142958	2.158744752	2.751397379
SR_S2_min	1.888268156	1.820199778	1.868317389	1.986740331	1.876941458
SR_S2_mean	3.452196028	3.774463247	3.822390994	3.992547223	3.702367762
SR_S2_max	5.65034965	6.227790433	6.366589327	7.113350126	6.043981481
SR_S2_sd	0.250354968	1.603764068	1.674547008	1.977904553	1.50825947
GNDVI_S2_min	0.343936382	0.334446764	0.336949718	0.351855527	0.333618299
GNDVI_S2_mean	0.552189436	0.567473272	0.686063723	0.559739718	0.558000471
GNDVI_S2_max	0.682749045	0.582229965	0.682038052	0.693553223	0.664552949
GNDVI_S2_sd	0.095852876	0.104511979	0.103932312	0.09843271	0.100702988
NDVI_S2_min	0.30754362	0.290830382	0.302727094	0.330373659	0.304817276
NDVI_S2_mean	0.516953664	0.533627389	0.535773089	0.537619621	0.532028678
NDVI_S2_max	0.699263933	0.723290252	0.728503937	0.753492704	0.716069668
NDVI_S2_sd	0.12634063	0.148072389	0.149401419	0.162739163	0.139558203
MSI_S2_min	0.81233244	0.788822355	0.794457275	0.668445122	0.802299867
MSI_S2_mean	0.930704234	0.90565302	0.906844201	0.8466494	0.933170817
MSI_S2_max	1.102307225	1.117755857	1.112196122	1.034373195	1.087325508
MSI_S2_sd	0.067017214	0.083339278	0.087646684	0.112269615	0.06634357
EVI_S2_min	0.689280278	0.656257524	0.679345729	0.607776519	0.711952972
EVI_S2_mean	1.29552207	1.376145802	1.389086554	1.358962858	1.406879394
EVI_S2_max	2.151187005	2.33378257	2.33155437	2.411753106	2.624040921
EVI_S2_sd	0.478965836	0.55427408	0.557920669	0.59002402	0.572866727
sampling_date	2021-03-	2021-03-	2022-02-	2022-02-	2022-02-
	20T00:00:00Z	20T00:00:00Z	12T00:00:00Z	12T00:00:00Z	12T00:00:00Z
taxorder	Alfisols	Alfisols	Alfisols	Alfisols	Alfisols
taxsuborder	Xeralfs	Xeralfs	Xeralfs	Xeralfs	Xeralfs
taxgrtgroup	Palexeralfs	Palexeralfs	Palexeralfs	Palexeralfs	Palexeralfs
taxsubgrp	Mollic	Mollic	Mollic	Mollic	Mollic
	Palexeralfs	Palexeralfs	Palexeralfs	Palexeralfs	Palexeralfs
taxpartsize	fine	fine	fine	fine	fine
lcsnt_r	3	3	3	3	3
awc_r	0.186046518	0.186046518	0.186046518	0.186046518	0.186046518
wthirdbar_r	32.77907083	32.77907083	32.77907083	32.77907083	32.77907083
wfifteenbar_r	19.71860415	19.71860415	19.71860415	19.71860415	19.71860415
kwfact	0.335116279	0.335116279	0.335116279	0.335116279	0.335116279
kffact	0.335116279	0.335116279	0.335116279	0.335116279	0.335116279
claytotal_r	30	30	30	30	30
musym	ZaA	ZaA	ZaA	RnA	RnA
ph_mean_0_5	6.825320244	5.702754498	6.55454731	6.085464478	6.041648855
clay_mean_0_5	30.61816406	28.04101563	17.84341431	20.12170029	18.84765525
sand_mean_0_5	33.76099777	21.79101563	40.79089355	33.76836395	38.62121201
silt_mean_0_5	34.049366	49.98537827	35.93652344	46.10312663	44.41789246
hb_mean_0_5	5.246660527	3.075604422	2.075308256	3.444456937	3.075338293
n_mean_0_5	1.258532047	1.283736467	1.355552021	1.321102142	1.334684134
alpha_mean_0_5	0.189423507	0.327131576	0.473978649	0.301981591	0.319007
ksat_mean_0_5	0.915640773	0.577488848	2.625437769	1.358792871	1.628046063
theta_r_mean_0_5	0.125004873	0.079575762	0.05575073	0.06526953	0.062726915
theta_s_mean_0_5	0.444021523	0.44546026	0.45504554	0.44041127	0.440598339
ph_mean_5_15	6.824072351	6.769343145	6.626858711	6.1234622	6.06571579
clay_mean_5_15	30.3465271	29.19463348	17.79268255	20.68449593	19.34082031
sand_mean_5_15	33.70332335	21.69373703	40.84082031	33.727005	38.33105469
silt_mean_5_15	34.05425252	48.85293961	36.04740906	45.42954636	44.07894897
hb_mean_5_15	5.304206965	3.196967429	2.050592208	3.501662862	3.084433696
n_mean_5_15	1.25922215	1.282382965	1.353484392	1.314134121	1.332590477
alpha_mean_5_15	0.189912993	0.31727985	0.468627346	0.292249271	0.318764365
ksat_mean_5_15	0.927740699	0.562644042	2.624737044	1.286311276	1.614845423
theta_r_mean_5_15	0.12429297	0.078746825	0.05562963	0.065474711	0.064448975
theta_s_mean_5_15	0.442747653	0.444574744	0.453072965	0.437219977	0.437517719
ph_mean_15_30	7.021382332	6.878724098	6.794202805	6.176373005	6.102796555
clay_mean_15_30	37.42074203	31.85288429	17.51750946	22.58815002	21.71616364
sand_mean_15_30	29.25268936	21.33251762	39.55278778	32.99027252	36.48627472
silt_mean_15_30	32.12854385	46.40820313	36.82226563	44.39247131	43.40244293
hb_mean_15_30	4.822539119	3.187482559	2.167941059	3.587482913	3.379542765
n_mean_15_30	1.242382765	1.272646308	1.361961365	1.316674709	1.326002836
alpha_mean_15_30	0.20894845	0.321051975	0.469476626	0.278972241	0.299199573
ksat_mean_15_30	0.537130041	0.480175669	2.543004733	0.99391625	1.141484023
theta_r_mean_15_30	0.134078592	0.085191405	0.052718445	0.055902442	0.065158717
theta_s_mean_15_30	0.464716391	0.44233945	0.448894173	0.428133726	0.430063009
ph_mode_0_5	7.18200015	5.364000092	6.998000145	5.710000038	5.710000038
clay_mode_0_5	31.5	31.5	22.5	11.5	11.5
sand_mode_0_5	34.5	7.5	12.5	42.5	42.5
silt_mode_0_5	32.5	48.5	53.5	45.5	45.5
hb_mode_0_5	6.575032681	5.199477081	1.485925851	5.199477081	3.251496509
n_mode_0_5	1.262500048	1.262500048	1.287499905	1.262500048	1.262500048
alpha_mode_0_5	0.165958702	0.218775179	0.660693437	0.19952821	0.288403176
ksat_mode_0_5	0.94055556	0.266397019	0.686152666	2.836336254	2.838336254
theta_r_mode_0_5	0.093999997	0.054000001	0.041999999	0.050000001	0.050000001
theta_s_mode_0_5	0.437735856	0.445283026	0.498113215	0.437735856	0.437735856
ph_mode_5_15	7.18200016	5.262000084	7.18200016	5.710000038	5.710000038
clay_mode_5_15	31.5	31.5	22.5	11.5	11.5
sand_mode_5_15	34.5	7.5	24.5	42.5	42.5
silt_mode_5_15	32.5	46.5	61.5	46.5	46.5
hb_mode_5_15	6.575032681	5.199477081	1.486925851	4.808175508	4.808175508
n_mode_5_15	1.262500048	1.237499952	1.287499905	1.287499905	1.287499905
alpha_mode_5_15	0.151356127	0.19952621	0.660693437	0.19952621	0.19952621
ksat_mode_5_15	0.94055556	0.227534596	0.586055563	2.836336254	2.836336254
theta_r_mode_5_15	0.093999997	0.07	0.041999999	0.050000001	0.050000001
theta_s_mode_5_15	0.437735856	0.437735856	0.490566045	0.437735856	0.422641546
ph_mode_15_30	7.366000175	6.630000114	7.642000198	5.618000031	5.618000031
clay_mode_15_30	39.5	32.5	21.5	12.5	12.5
sand_mode_15_30	29.5	6.5	25.5	43.5	43.5
silt_mode_15_30	30.5	42.5	60.5	45.5	45.5
hb_mode_15_30	5.080208889	5.199477081	1.607935775	4.808175508	4.808175508
n_mode_15_30	1.237499952	1.237499952	1.287499905	1.287499905	1.287499905
alpha_mode_15_30	0.165958702	0.181970082	0.602559588	0.19952621	0.19952621
ksat_mode_15_30	0.586055563	0.165990684	0.500560874	0.165990684	2.42256715
theta_r_mode_15_30	0.106000006	0.07	0.041999999	0.050000001	0.050000001
theta_s_mode_15_30	0.480377366	0.445283026	0.475471735	0.445283026	0.445283026
ph_p50_0_5	6.81400013	6.446000099	6.538000107	5.986000061	5.802000046
clay_p50_0_5	30.5	30.5	18.5	16.5	15.5
sand_p50_0_5	34.5	20.5	30.5	40.5	41.5
silt_p50_0_5	32.5	45.5	36.5	45.5	44.5
hb_p50_0_5	5.199477081	3.251496509	1.880301532	3.516112061	3.006795599
n_p50_0_5	1.237499952	1.262500048	1.3125	1.287499905	1.3125
alpha_p50_0_5	0.181970082	0.288403176	0.50118722	0.263026773	0.288403176
ksat_p50_0_5	0.803345892	0.585055563	2.06915932	2.06915932	2.06915932
theta_r_p50_0_5	0.118000001	0.077999994	0.050000001	0.061999999	0.067999998
theta_s_p50_0_5	0.437735856	0.437735856	0.460377365	0.437735856	0.437735856
ph_p50_5_15	6.181400013	6.446000099	6.538000107	5.986000061	5.802000046
clay_p50_5_15	30.5	30.5	18.5	16.5	15.5
sand_p50_5_15	34.5	19.5	27.5	39.5	40.5
silt_p50_5_15	32.5	45.5	37.5	45.5	44.5
hb_p50_5_15	5.199477081	3.251496500	1.880301532	3.802262741	3.251496509
n_p50_5_15	1.237499952	1.262500048	1.3125	1.287499905	1.3125
alpha_p50_5_15	0.181970082	0.288403176	0.50118722	0.263026773	0.288403175
ksat_p50_5_15	0.803345892	0.586055563	1.767307169	2.06916932	2.06915932
theta_r_p50_5_15	0.118000001	0.077999994	0.050000001	0.061999999	0.057999998
theta_s_p50_5_15	0.437735856	0.437735856	0.467924555	0.430188715	0.430188715
ph_p50_15_30	8.998000145	8.630000114	8.630000114	6.170000076	5.710000038
clay_p50_15_30	38.5	31.5	18.5	19.5	19.5
sand_p50_15_30	18.5	19.5	25.5	37.5	39.5
silt_p50_15_30	30.5	42.5	41.5	42.5	44.5
hb_p50_15_30	4.808175508	3.251495509	1.880301532	3.802262741	3.516112061
n_p50_15_30	1.212500095	1.262500048	1.3125	1.287499905	1.287499905
alpha_p50_15_30	0.19952521	0.288403176	0.50118722	0.239883289	0.263026773
ksat_p50_15_30	0.500560874	0.585055563	2.06915932	1.289282995	1.289282995
theta_r_p50_15_30	0.129999995	0.082000002	0.046472457	0.066	0.061999999
theta_s_p50_15_30	0.460377365	0.437735856	0.457924565	0.430188715	0.430188715
ph_p5_0_5	5.894000053	1.845999956	5.710000038	5.342000008	5.342000008
clay_p5_0_5	21.5	14.5	2.5	8.5	9.5
sand_p5_0_5	14.5	5.50E+00	5.50E+00	6.5	7.5
silt_p5_0_5	0.5	34.5	6.5	0.5	15.5
hb_p5_0_5	2.671254732	0.795159229	0.735317163	1.087355863	1.005523679
n_p5_0_5	1.162499905	1.162499905	1.212500095	1.1875	1.212500095
alpha_p5_0_5	0.066069335	0.096499263	0.138038422	0.095499263	0.10471285
ksat_p5_0_5	0.586055563	3.90E−05	3.90E−05	0.194341485	0.194341485
theta_r_p5_0_5	0.01	0.002	0.002	0.002	0.002
theta_s_p5_0_5	0.060377389	0.430188715	0.384905696	0.422641546	0.415094376
ph_p5_5_15	5.986000061	6.170000075	5.802000046	5.434000015	5.434000015
clay_p5_5_15	21.5	15.5	2.5	10.5	10.5
sand_p5_5_15	14.5	6.50E+00	5.50E+00	7.5	7.5
silt_p5_5_15	0.5	34.5	8.5	0.5	16.5
hb_p5_5_15	2.571254732	0.869871401	0.795159229	1.087355863	0.929850015
n_p5_5_15	1.162499905	1.1875	1.212500095	1.1875	1.212500095
alpha_p5_5_15	0.066069335	0.095499263	0.138038422	0.095499263	0.10471285
ksat_p5_5_15	0.586055553	3.90E−05	3.90E−05	0.194341485	0.194341485
theta_r_p5_5_15	0.01	0.002	0.002	0.002	0.002
theta_s_p5_5_15	0.060377389	0.430188715	0.384905698	0.415094376	0.415094376
ph_p5_15_30	6.354000092	6.262000084	5.802000046	5.342000008	5.342000008
clay_p5_15_30	21.5	19.5	1.5	10.5	10.5
sand_p5_15_30	15.5	5.00E−01	5.50E+00	6.50E+00	6.50E+00
silt_p5_15_30	0.5	33.5	5.5	0.5	18.5
hb_p5_15_30	2.571254732	0.859871401	0.795159229	1.087355863	1.087355863
n_p5_15_30	1.137500048	1.62499905	1.212500095	1.1875	1.1875
alpha_p5_15_30	0.10471285	0.095499263	0.138038422	0.095499263	0.095499263
ksat_p5_15_30	0.311897019	3.90E−05	3.90E−05	3.90E−05	3.90E−05
theta_r_p5_15_30	0.002	0.006	0.002	0.006	0.006
theta_s_p5_15_30	0.445283026	0.060377389	0.377358526	0.384905696	0.384905696
ph_p95_0_5	7.274000168	7.642000198	6.998000145	6.998000145	6.998000145
clay_p95_0_5	33.5	31.5	29.5	31.5	31.5
sand_p95_0_5	37.5	39.5	76.5	43.5	67.5
silt_p95_0_5	37.5	64.5	61.5	61.5	48.5
hb_p95_0_5	14.37778402	9.722878889	6.080208889	9.722878889	8.314499354
n_p95_0_5	1.337500095	1.412499905	1.537499905	1.452500095	1.487499952
alpha_p95_0_5	0.346736868	1.148153618	1.258925395	0.954992587	0.954992587
ksat_p95_0_5	1.767307169	2.636335254	25.79314315	3.320776433	3.320776433
theta_r_p95_0_5	0.205	0.129999995	0.114	0.118000001	0.114
theta_s_p95_0_5	0.475471735	0.460377365	0.498113215	0.460377365	0.460377365
ph_p95_5_15	7.16200015	7.734000205	7.18200016	6.998000145	6.998000145
clay_p95_5_15	32.5	34.5	29.5	34.5	32.5
sand_p95_5_15	34.5	38.5	77.5	43.5	65.5
silt_p95_5_15	37.5	61.5	51.5	46.5	46.5
hb_p95_5_15	14.37778402	9.722878889	6.080208889	8.991155729	8.314499354
n_p95_5_15	1.337500095	1.412499905	1.537499905	1.4375	1.487499952
alpha_p95_5_15	0.346736868	1.148153618	1.148153518	0.954992587	0.95499587
ksat_p95_5_15	1.767307169	2.836336254	25.79314316	2.835336254	3.320776433
theta_r_p95_5_15	0.206	0.129999995	0.109999999	0.118000001	0.118000001
theta_s_p95_5_15	0.475471735	0.460377355	0.490566045	0.460377366	0.467924565
ph_p95_15_30	7.366000175	7.918000221	7.642000198	6.998000145	6.998000145
clay_p95_15_30	40.5	39.5	28.5	39.5	38.5
sand_p95_15_30	29.5	36.5	86.5	44.5	56.5
silt_p95_15_30	38.5	61.5	60.5	52.5	52.5
hb_p95_15_30	8.314499354	9.722878889	6.080208889	9.722878889	9.722878889
n_p95_15_30	1.362499952	1.387500048	1.5625	1.462500095	1.487499952
alpha_p95_15_30	0.380189382	1.148153618	1.148153618	0.870963593	0.954992587
ksat_p95_15_30	1.101200404	2.42256715	25.79314316	2.836336254	2.836336254
theta_r_p95_15_30	0.233999997	0.134000003	0.101999998	0.114	0.114
theta_s_p95_15_30	0.475471735	0.450377365	0.483018875	0.460377365	0.460377365
wrb	Lixisols	Lixisols	Lixisols	Lixisols	Lixisols
cec_0_5 cm_Q0.05	96	96	96	96	96
cec_5_15 cm_Q0.05	100	100	100	100	100
cec_15_30 cm_Q0.05	93	93	93	93	93
cec_30_60 cm_Q0.05	93	93	93	93	93
cec_60_100 cm_Q0.05	101	101	101	101	101
cec_100_200 cm_Q0.05	115	115	115	115	115
clay_0_5 cm_Q0.05	26	26	26	26	26
clay_5_15 cm_Q0.05	32	32	32	32	32
clay_15_30 cm_Q0.05	51	51	51	51	51
clay_30_60 cm_Q0.05	39	39	39	39	39
clay_60_100 cm_Q0.05	35	35	35	35	35
clay_100_200 cm_Q0.05	29	29	29	29	29
phh2o_0_5 cm_Q0.05	51	51	51	51	51
phh2o_5_15 cm_Q0.05	51	51	51	51	51
phh2o_15_30 cm_Q0.05	51	51	51	51	51
phh2o_30_60 cm_Q0.05	51	51	51	51	51
phh2o_60_100 cm_Q0.05	50	50	50	50	50
phh2o_100_200 cm_Q0.05	50	50	50	50	50
sand_0_5 cm_Q0.05	20	20	20	20	20
sand_5_15 cm_Q0.05	20	20	20	20	20
sand_15_30 cm_Q0.05	20	20	20	20	20
sand_30_60 cm_Q0.05	16	16	16	16	16
sand_60_100 cm_Q0.05	15	15	15	15	15
sand_100_200 cm_Q0.05	16	16	16	16	16
silt_0_5 cm_Q0.05	41	41	41	41	41
silt_5_15 cm_Q0.05	42	42	42	42	42
silt_15_30 cm_Q0.05	33	33	33	33	33
silt_30_60 cm_Q0.05	39	39	39	39	39
silt_60_100 cm_Q0.05	27	27	27	27	27
silt_100_200 cm_Q0.05	19	19	19	19	19
cec_0_5 cm_Q0.5	195	195	195	195	195
cec_5_15 cm_Q0.5	190	190	190	190	190
cec_15_30 cm_Q0.5	183	183	183	183	183
cec_30_60 cm_Q0.5	195	195	195	195	195
cec_60_100 cm_Q0.5	234	234	234	234	234
cec_100_200 cm_Q0.5	230	230	230	230	230
cfvo_0_5 cm_Q0.5	10	10	10	10	10
cfvo_5_15 cm_Q0.5	30	30	30	30	30
cfvo_15_30 cm_Q0.5	10	10	10	10	10
cfvo_30_60 cm_Q0.5	10	10	10	10	10
cfvo_60_100 cm_Q0.5	10	10	10	10	10
cfvo_100_200 cm_Q0.5	10	10	10	10	10
clay_0_5 cm_Q0.5	222	222	222	222	222
clay_5_15 cm_Q0.5	228	228	228	228	228
clay_15_30 cm_Q0.5	295	295	295	295	295
clay_30_60 cm_Q0.5	302	302	302	302	302
clay_60_100 cm_Q0.5	310	310	310	310	310
clay_100_200 cm_Q0.5	306	306	306	306	306
phh2o_0_5 cm_Q0.5	60	60	60	60	60
phh2o_5_15 cm_Q0.5	60	60	60	60	60
phh2o_15_30 cm_Q0.5	60	60	60	60	60
phh2o_30_60 cm_Q0.5	60	60	60	60	60
phh2o_60_100 cm_Q0.5	60	60	60	60	60
phh2o_100_200 cm_Q0.5	63	63	63	63	63
sand_0_5 cm_Q0.5	242	242	242	242	242
sand_5_15 cm_Q0.5	242	242	242	242	242
sand_15_30 cm_Q0.5	223	223	223	223	223
sand_30_60 cm_Q0.5	217	217	217	217	217
sand_60_100 cm_Q0.5	220	220	220	220	220
sand_100_200 cm_Q0.5	217	217	217	217	217
silt_0_5 cm_Q0.5	399	399	399	399	399
silt_5_15 cm_Q0.5	402	402	402	402	402
silt_15_30 cm_Q0.5	372	372	372	372	372
silt_30_60 cm_Q0.5	370	370	370	370	370
silt_60_100 cm_Q0.5	325	325	325	325	325
silt_100_200 cm_Q0.5	317	317	317	317	317
cec_0_5 cm_mean	219	219	219	219	219
cec_5_15 cm_mean	192	192	192	192	192
cec_15_30 cm_mean	187	187	187	187	187
cec_30_60 cm_mean	201	201	201	201	201
cec_60_100 cm_mean	239	239	239	239	239
cec_100_200 cm_mean	234	234	234	234	234
cfvo_0_5 cm_mean	61	61	61	61	61
cfvo_5_15 cm_mean	75	75	75	75	75
cfvo_15_30 cm_mean	58	58	58	58	58
cfvo_30_60 cm_mean	60	60	60	60	60
cfvo_60_100 cm_mean	55	55	55	55	55
cfvo_100_200 cm_mean	60	60	60	60	60
clay_0_5 cm_mean	333	333	333	333	333
clay_5_15 cm_mean	330	330	330	330	330
clay_15_30 cm_mean	386	386	386	386	386
clay_30_60 cm_mean	388	388	388	388	388
clay_60_100 cm_mean	394	394	394	394	394
clay_100_200 cm_mean	383	383	383	383	383
phh2o_0_5 cm_mean	62	62	62	62	62
phh2o_5_15 cm_mean	61	61	61	61	61
phh2o_15_30 cm_mean	61	61	61	61	61
phh2o_30_60 cm_mean	61	61	61	61	61
phh2o_60_100 cm_mean	62	62	62	62	62
phh2o_100_200 cm_mean	63	63	63	63	63
sand_0_5 cm_mean	246	246	246	246	246
sand_5_15 cm_mean	247	247	247	247	247
sand_15_30 cm_mean	233	233	233	233	233
sand_30_60 cm_mean	234	234	234	234	234
sand_60_100 cm_mean	243	243	243	243	243
sand_100_200 cm_mean	257	257	257	257	257
silt_0_5 cm_mean	421	421	421	421	421
silt_5_15 cm_mean	423	423	423	423	423
silt_15_30 cm_mean	381	381	381	381	381
silt_30_60 cm_mean	378	378	378	378	378
silt_60_100 cm_mean	364	364	364	364	364
silt_100_200 cm_mean	360	360	360	360	360
cec_0_5 cm_Q0.95	378	378	378	378	378
cec_5_15 cm_Q0.95	323	323	323	323	323
cec_15_30 cm_Q0.95	330	330	330	330	330
cec_30_60 cm_Q0.95	333	333	333	333	333
cec_60_100 cm_Q0.95	377	377	377	377	377
cec_100_200 cm_Q0.95	377	377	377	377	377
cfvo_0_5 cm_Q0.95	240	240	240	240	240
cfvo_5_15 cm_Q0.95	291	291	291	291	291
cfvo_15_30 cm_Q0.95	272	272	272	272	272
cfvo_30_60 cm_Q0.95	312	312	312	312	312
cfvo_60_100 cm_Q0.95	234	234	234	234	234
cfvo_100_200 cm_Q0.95	358	358	358	358	358
clay_0_5 cm_Q0.95	935	935	935	935	935
clay_5_15 cm_Q0.95	917	917	917	917	917
clay_15_30 cm_Q0.95	939	939	939	939	939
clay_30_60 cm_Q0.95	939	939	939	939	939
clay_60_100 cm_Q0.95	940	940	940	940	940
clay_100_200 cm_Q0.95	959	959	959	959	959
phh2o_0_5 cm_Q0.95	75	75	75	75	75
phh2o_5_15 cm_Q0.95	75	75	75	75	75
phh2o_15_30 cm_Q0.95	75	75	75	75	75
phh2o_30_60 cm_Q0.95	77	77	77	77	77
phh2o_60_100 cm_Q0.95	79	79	79	79	79
phh2o_100_200 cm_Q0.95	80	80	80	80	80
sand_0_5 cm_Q0.95	545	545	545	545	545
sand_5_15 cm_Q0.95	551	551	551	551	551
sand_15_30 cm_Q0.95	549	549	549	549	549
sand_30_60 cm_Q0.95	550	550	550	550	550
sand_60_100 cm_Q0.95	572	572	572	572	572
sand_100_200 cm_Q0.95	608	608	608	608	608
silt_0_5 cm_Q0.95	898	898	898	898	898
silt_5_15 cm_Q0.95	892	892	892	892	892
silt_15_30 cm_Q0.95	871	871	871	871	871
silt_30_60 cm_Q0.95	886	886	886	886	886
silt_60_100 cm_Q0.95	871	871	871	871	871
silt_100_200 cm_Q0.95	884	884	884	884	884

	Parcel V4
	Sample Location

	V4-P-2	V4-P-10	V4-P-5	V4-P-7	V4-P-4
LCI_max	0.634557788	0.551017374	0.525584118	0.491651093	0.563030012
LCI_sd	0.173045306	0.158036973	0.128085251	0.109302059	0.112738076
EVI_min	0.075545421	0.055322408	0.074289835	0.067785363	0.050029924
EVI_mean	0.319518089	0.238987423	0.335786666	0.168519919	0.246823639
EVI_max	0.714437521	0.584214733	0.641753749	0.501755983	0.514998371
EVI_sd	0.193583024	0.155443401	0.134625897	0.10098445	0.114019032
NIRv_min	0.08444199	0.095278546	0.065097526	0.078875817	0.050712764
NIRv_mean	0.211793533	0.194651665	0.221897934	0.175615454	0.175114542
NIRv_max	0.449568123	0.361375928	0.378113359	0.514488173	0.312473327
NIRv_sd	0.091183441	0.062014803	0.060828597	0.038533625	0.05306091
GLI_min	−0.019670436	−0.050347676	−0.023624289	−0.047226636	−0.033135772
GLI_mean	0.171185624	0.097525666	0.169545200	0.036023775	0.099468536
GLI_max	0.438069746	0.357771255	0.432628262	0.30358203	0.324431248
GLI_sd	0.145660167	0.130338934	0.115081174	0.077061655	0.094597551
CVI_min	2.407390977	2.523473974	2.511619416	2.565787369	2.403864699
CVI_mean	3.480831976	3.166495639	3.697953172	3.384802811	3.626291457
CVI_max	5.229198166	4.431450855	5.485914347	6.179979512	5.071275116
CVI_sd	0.710215355	0.426959432	0.576088531	0.545306223	0.498729401
CI_Rededge_min	0.177617428	0.138069593	0.293501513	0.163561557	0.268518894
CI_Rededge_mean	0.956050176	0.659489338	0.961467189	0.459020524	0.795520343
CI_Rededge_max	2.058848336	1.563100218	1.961764331	1.222684537	1.609234431
CI_Rededge_sd	0.523377434	0.412885108	0.38304724	0.260820334	0.31244898
NDRE_min	0.081565028	0.054576754	0.127970925	0.075598296	0.118406355
NDRE_mean	0.301301225	0.230412901	0.312845355	0.178407393	0.275608012
NDRE_max	0.507249388	0.438691062	0.495174414	0.379399406	0.445865865
NDRE_sd	0.125075398	0.113738666	0.092213788	0.078224175	0.080670399
system	Vineyard	Vineyard	Vineyard	Vineyard	Vineyard
crop	Wine grapes	Wine grapes	Wine grapes	Wine grapes	Wine grapes
age	2	2	2	2	2
prcp_mean	7.929999985	7.929999985	7.929999985	7.929999985	7.929999985
prcp_max	105.8899994	105.8899994	105.8899994	105.8899994	105.8899994
prcp_sd	9.085575635	9.085575635	9.085575635	9.085575635	9.085575635
srad_min	77.40000153	77.40000153	77.40000153	77.40000153	77.40000153
srad_mean	267.9093065	267.9093065	267.9093065	267.9093065	267.9093065
srad_max	442.019989	442.019989	442.019989	442.019989	442.019989
srad_sd	85.9787663	85.9787663	85.9787663	85.9787663	85.9787663
tmax_min	8.300000191	8.300000191	8.300000191	8.300000191	8.300000191
tmax_mean	19.88942193	19.88942193	19.88942193	19.88942193	19.88942193
tmax_max	38.5	38.5	38.5	38.5	38.5
tmax_sd	6.68582569	6.68582569	6.68582569	6.68582569	6.68582569
tmin_min	−2.109999895	−2.109999895	−2.109999895	−2.109999895	−2.109999895
tmin_mean	7.15433525	7.15433525	7.15433525	7.15433525	7.15433525
tmin_max	20.09000015	20.09000015	20.09000015	20.09000015	20.09000015
tmin_sd	4.423895763	4.423895763	4.423895763	4.423895763	4.423895763
vp_min	306.2200012	306.2200012	306.2200012	306.2200012	306.2200012
vp_mean	820.4393046	820.4393046	820.4393046	820.4393046	820.4393046
vp_max	1445.920044	1445.920044	1445.920044	1445.920044	1445.920044
vp_sd	238.8830191	238.8830191	238.8830191	238.8830191	238.8830191
sunlight_hr_mean	10.55606162	10.55606162	10.55606162	10.55606162	10.55606162
sunlight_hr_sum	1835.754722	1835.754722	1835.754722	1835.754722	1835.754722
sampling_date.y	2022-03-02	2022-03-02	2022-03-02	2022-03-02	2022-03-02
SCI_S2_min	−0.286644951	−0.265433513	−0.295116323	−0.270616663	−0.19535547
SCI_S2_mean	−0.008214213	−0.005852271	−0.030949549	−0.039393524	−0.027515988
SCI_S2_max	0.235901509	0.24187356	0.220246238	0.207137315	0.180485339
SCI_S2_sd	0.19016092	0.1784377	0.177823329	0.157882553	0.121341241
CI_S2_min	0.214555256	0.220871327	0.223454834	0.190998902	0.216981132
CI_S2_mean	0.430608349	0.440188669	0.440885848	0.435452852	0.432282685
CI_S2_max	0.632478632	0.696935795	0.667889908	0.622425529	0.515566038
CI_S2_sd	0.139941394	0.136932434	0.146031512	0.135704006	0.14346231
NDWI_S2_min	−0.758415842	−0.74789915	−0.730451367	−0.755753877	−0.745323741
NDWI_S2_mean	−0.53749341	−0.636065508	−0.542129613	−0.546076639	−0.545017417
NDWI_S2_max	−0.292191436	−0.294068505	−0.30551844	−0.297841123	−0.300083822
NDWI_S2_sd	0.162148331	0.169774155	0.150353808	0.159248407	0.154302257
VARI_S2_min	−0.290640541	−0.290512175	−0.325280414	−0.264454976	−0.168918919
VARI_S2_mean	−0.121260219	−0.108398296	−0.102440029	−0.104866986	−0.085419764
VARI_S2_max	−0.014258555	−0.014925373	−0.029325513	−0.008415147	0
VARI_S2_sd	0.060520614	0.067010357	0.062346234	0.058710512	0.041594432
kNDVI_S2_min	0.056975134	0.060436992	0.063678693	0.054681631	0.066418375
kNDVI_S2_mean	0.243103347	0.245809432	0.252853902	0.258079158	0.265088141
kNDVI_S2_max	0.481219216	0.47965895	0.457972244	0.503675858	0.471678313
kNDVI_S2_sd	0.152579935	0.155053695	0.142036195	0.153474283	0.143929021
SAVI_S2_min	0.35819994	0.368948725	0.378738525	0.350901664	0.366622098
SAVI_S2_mean	0.713235179	0.717333173	0.73397608	0.739099276	0.753706457
SAVI_S2_max	1.085232001	1.079150897	1.054903801	1.116506436	1.073360438
SAVI_S2_sd	0.250529163	0.263115106	0.241957666	0.259196835	0.244154805
ENDVI_S2_min	0.235270962	0.237859267	0.246057187	0.249116376	0.25413929
ENDVI_S2_mean	0.52851363	0.536826187	0.545454636	0.54418281	0.551691952
ENDVI_S2_max	0.778249015	0.777843778	0.787496331	0.797421731	0.781574539
ENDVI_S2_sd	0.192443602	0.19251828	0.186675981	0.191929235	0.187949853
LCI_S2_B5_min	0.171497585	0.178399035	0.18124383	0.181546433	0.177971375
LCI_S2_B5_mean	0.357640653	0.366672623	0.363213132	0.36093291	0.360797402
LCI_S2_B5_max	0.543848776	0.533899138	0.523162446	0.548891786	0.530763791
LCI_S2_B5_sd	0.13760679	0.133087855	0.122845646	0.130646894	0.124757047
LCI_S2_B6_min	0.070249597	0.077722278	0.07798618	0.05981717	0.050689376
LCI_S2_B6_mean	0.151940901	0.147343924	0.151002842	0.133643196	0.126244803
LCI_S2_B6_max	0.22038835	0.215931534	0.211201502	0.235332464	0.230551627
LCI_S2_B6_sd	0.046920371	0.04427192	0.040269077	0.057244301	0.066951105
LCI_S2_B7_min	0.020128824	0.037659445	0.034550839	−0.020296643	−0.062597201
LCI_S2_B7_mean	0.055362643	0.062189997	0.068324593	0.046509774	0.037156758
LCI_S2_B7_max	0.109634551	0.104344964	0.103328051	0.128699701	0.128666689
LCI_S2_B7_sd	0.025117829	0.020519165	0.019376803	0.036344945	0.050469625
NIRv_S2_min	1324	1436	1386	1408	1405
NIRv_S2_mean	2430.9	2463.8	2511.55	2487.05	2436.8
NIRv_S2_max	3176	3262	3309	3330	3504
NIRv_S2_sd	725.5072101	664.3532351	718.9040984	683.1260641	639.2972787
GLI_S2_min	−0.038961039	−0.046815042	−0.069354839	−0.025076991	0.001691007
GLI_S2_mean	0.037302392	0.049077714	0.053274432	0.050269028	0.062957514
GLI_S2_max	0.110047847	0.137534247	0.116049383	0.128425578	0.143174251
GLI_S2_sd	0.042719626	0.051251812	0.04972515	0.046496192	0.039946562
CVI_S2_min	2.046516635	2.032720143	2.099585077	2.116794459	2.035447668
CVI_S2_mean	4.629295426	4.471459626	4.447454042	4.613407898	4.426290478
CVI_S2_max	8.471916152	8.531851352	7.517241379	8.752913143	7.782342239
CVI_S2_sd	2.08662161	1.962831215	1.781147828	2.058582532	1.917639276
CI_Rededge_S2_B5_min	0.382749326	0.401175938	0.407275954	0.416933638	0.393217232
CI_Rededge_S2_B5_mean	1.007844967	0.981740551	0.999388554	0.988453315	0.97158566
CI_Rededge_S2_B5_max	1.796315251	1.68902439	1.631490787	1.697580845	1.622702703
CI_Rededge_S2_B5_sd	0.517096669	0.470351855	0.438289626	0.465711921	0.436626283
CI_Rededge_S2_B6_min	0.127885572	0.141505002	0.142239827	0.105799649	0.076464208
CI_Rededge_S2_B6_mean	0.256295213	0.246293671	0.251913208	0.216026607	0.202520921
CI_Rededge_S2_B6_max	0.347711731	0.335378323	0.329750855	0.369498465	0.372334609
CI_Rededge_S2_B6_sd	0.067296395	0.063431252	0.055850405	0.086346662	0.107427024
CI_Rededge_S2_B7_min	0.033579584	0.05967575	0.058391725	−0.026666667	−0.075023299
CI_Rededge_S2_B7_mean	0.098614666	0.09180069	0.1007852	0.065890388	0.052648371
CI_Rededge_S2_B7_max	0.15502451	0.142671855	0.142732049	0.177278974	0.179723502
CI_Rededge_S2_B7_sd	0.035633826	0.028402681	0.025488142	0.048238761	0.067456104
NDRE_S2_B5_min	0.160633484	0.167074779	0.169185404	0.172505207	0.164304854
NDRE_S2_B5_mean	0.316647973	0.313233744	0.319469966	0.315338951	0.312999097
NDRE_S2_B5_max	0.473173362	0.45785124	0.449261993	0.45910578	0.447925992
NDRE_S2_B5_sd	0.113644116	0.107593116	0.099532877	0.104783348	0.100598211
NDRE_S2_B6_min	0.050099879	0.066077799	0.066397714	0.05024203	0.036824236
NDRE_S2_B6_mean	0.112833802	0.108957118	0.111339294	0.096195917	0.089935635
NDRE_S2_B6_max	0.148106655	0.143607706	0.141539107	0.155939525	0.156948606
NDRE_S2_B6_sd	0.026738914	0.025627096	0.022372403	0.034803187	0.043480332
NDRE_S2_B7_min	0.01651255	0.028973371	0.028368545	−0.013513514	−0.038973614
NDRE_S2_B7_mean	0.046729264	0.04371964	0.047841939	0.031397642	0.024655171
NDRE_S2_B7_max	0.071936309	0.066585956	0.066612178	0.08142226	0.082452431
NDRE_S2_B7_sd	0.016189005	0.012894675	0.011546308	0.02240774	0.031854362
Three_BSI_Tian_S2_min	−0.813223566	−0.809378408	−0.805751492	−0.803200692	−0.801833261
Three_BSI_Tian_S2_mean	−0.707795049	−0.707147112	−0.693023119	−0.705585737	−0.713095954
Three_BSI_Tian_S2_max	−0.568392371	−0.584051135	−0.552060231	−0.585709042	−0.580305927
Three_BSI_Tian_S2_sd	0.081944198	0.078220992	0.080203987	0.077792424	0.079407031
mND_Verrelat_S2_max	9.339181287	6.883333333	9.169491525	5.649819495	6.382606696
MCARI_S2_min	30451.2	30655.2	30764.8	42343	53125.8
MCARI_S2_mean	116630.15	132315.12	138853.82	143475.97	149519.35
MCARI_S2_max	278066.8	337524	330865.2	305584.8	282973.6
MCARI_S2_sd	90486.61313	104640.5866	99550.98054	98964.64486	69309.32823
IRECI_S2_min	794.109589	910.1322816	998.8095839	1065.668858	1164.540748
IRECI_S2_mean	2250.377705	2270.222002	2301.725514	2427.684823	2410.33725
IRECI_S2_max	5045.468777	4805.746073	4671.961093	4610.928277	4146.920175
IRECI_S2_sd	1503.006562	1400.03622	1323.297038	1254.720644	947.1223035
NDMI_S2_min	−0.235901509	−0.24187356	−0.220246238	−0.207137316	−0.180485339
NDMI_S2_mean	0.008214213	0.005852271	0.030949549	0.039393524	0.027516988
NDMI_S2_max	0.286644951	0.255433513	0.286116323	0.270615563	0.19535547
NDMI_S2_sd	0.19016092	0.1784377	0.177823329	0.157882553	0.121341241
S2REP_min	712.2474747	709.9875	714.3201754	711.6268382	712.0845921
S2REP_mean	719.2690839	718.8489845	718.6293745	718.8039754	718.3447813
S2REP_max	722.9038462	721.9038929	721.2389912	721.3354317	720.895967
S2REP_sd	2.431737045	2.683367325	1.746762111	2.227869518	2.072721337
SR_S2_min	1.627513228	1.65248227	1.675647121	1.61082205	1.695081967
SR_S2_mean	3.292044316	3.330829745	3.32606391	3.453801284	3.459220865
SR_S2_max	6.253521127	6.131455399	5.742616034	6.826530512	6.034825871
SR_S2_sd	1.573402864	1.606039889	1.404833231	1.644154371	1.450994315
GNDVI_S2_min	0.292191436	0.294068505	0.30561844	0.297841123	0.300083822
GNDVI_S2_mean	0.53749341	0.535065608	0.642129613	0.546075539	0.545017417
GNDVI_S2_max	0.758415842	0.74789916	0.730451367	0.765753877	0.745323741
GNDVI_S2_sd	0.162148331	0.159774165	0.150363808	0.159248407	0.154302257
NDVI_S2_min	0.238824003	0.245989305	0.252517275	0.233957752	0.257907543
NDVI_S2_mean	0.475571699	0.478302222	0.489399638	0.492815886	0.50255778
NDVI_S2_max	0.724271845	0.719552337	0.703379224	0.744458931	0.715700141
NDVI_S2_sd	0.173713656	0.175439916	0.161333143	0.172827836	0.162801775
MSI_S2_min	0.55443038	0.593075205	0.555089292	0.574040219	0.673142468
MSI_S2_mean	1.049031921	1.046875167	0.993609908	0.965935534	0.972771761
MSI_S2_max	1.617463617	1.638082377	1.564912281	1.522504892	1.440468846
MSI_S2_sd	0.357237271	0.350167931	0.330006767	0.291425256	0.23637186
EVI_S2_min	0.52668391	0.548924787	0.533872599	0.696377307	0.71318361
EVI_S2_mean	1.053500418	1.043615368	1.070005015	1.086405508	1.119725298
EVI_S2_max	1.51277545	1.501252784	1.504510755	1.590972415	1.555952742
EVI_S2_sd	0.299978463	0.299562502	0.264417963	0.29105597	0.250241911
sampling_date	2022-03-	2022-03-	2022-03-	2022-03-	2022-03-
	02T00:00:00Z	02T00:00:00Z	02T00:00:00Z	02T00:00:00Z	02T00:00:00Z
taxorder	Alfisols	Alfisols	Alfisols	Alfisols	Alfisols
taxsuborder	Xeralfs	Xeralfs	Xeralfs	Xeralfs	Xeralfs
taxgrtgroup	Dunxeralfs	Dunxeralfs	Dunxeralfs	Dunxeralfs	Dunxeralfs
taxsubgrp	Abruptic	Abruptic	Abruptic	Abruptic	Abruptic
	Dunxeralfs	Dunxeralfs	Dunxeralfs	Dunxeralfs	Dunxeralfs
taxpartsize	fine	fine	fine	fine	fine
lcsnt_r	9	9	9	9	9
awc_r	0.119999997	0.119999997	0.119999997	0.119999997	0.119999997
wthirdbar_r	19.558139	19.558139	19.558139	19.558139	19.558139
wfifteenbar_r	8.646511865	8.646511865	8.646511865	8.646511865	8.646511865
kwfact	0.227906977	0.227906977	0.227906977	0.227906977	0.227906977
kffact	0.471860465	0.471860465	0.471860465	0.471860465	0.471860465
claytotal_r	16.09302326	16.09302326	16.09302326	16.09302326	16.09302326
musym	221	221	221	221	221
ph_mean_0_5	6.83998251	5.579891205	5.659377575	5.429769039	5.76255846
clay_mean_0_5	12.26953125	11.15527344	12.87243366	13.33446884	11.39355469
sand_mean_0_5	44.21722031	55.81769562	42.36190033	40.43164063	55.8542099
silt_mean_0_5	39.84570313	59.13147736	39.826828	40.98194885	57.6541481
hb_mean_0_5	1.435451871	1.487495829	1.407700782	1.358189533	1.527814411
n_mean_0_5	1.444977999	1.392428729	1.434880018	1.433837891	1.390307665
alpha_mean_0_5	0.70622863	0.669529922	0.711239036	0.746932326	0.651631315
ksat_mean_0_5	1.572783163	5.262482819	1.466581585	1.316189737	3.373450362
theta_r_mean_0_5	0.041882701	0.043558594	0.044835295	0.046074368	0.043986343
theta_s_mean_0_5	0.435584813	0.525374234	0.45305109	0.462408155	0.521392822
ph_mean_5_15	5.897957325	5.705063343	5.72110796	5.540057182	5.32605505
clay_mean_5_15	12.35575006	11.04003906	12.8891103	13.10878086	11.28849888
sand_mean_5_15	43.7819519	55.29587936	42.17580795	40.23170471	55.37155577
silt_mean_5_15	40.01026917	58.69024277	40.0450058	41.28396606	56.34408569
hb_mean_5_15	1.465882402	1.393925777	1.523085129	1.501167291	1.465208588
n_mean_5_15	1.446419954	1.397939086	1.436815143	1.432529449	1.395085335
alpha_mean_5_15	0.680436362	0.715877477	0.655739753	0.654214036	0.688052913
ksat_mean_5_15	1.389473918	6.324563701	1.238686192	1.063372226	3.28174714
theta_r_mean_5_15	0.039874509	0.043348379	0.045458294	0.045737259	0.043760933
theta_s_mean_5_15	0.421829998	0.523228765	0.426632315	0.435714424	0.517851949
ph_mean_15_30	6.058990479	6.405271053	5.92124939	5.776344299	6.481218815
clay_mean_15_30	15.32859993	11.23607083	14.16144753	16.73874664	11.51759529
sand_mean_15_30	42.00584412	57.02889252	40.59970856	37.74511719	58.98532104
silt_mean_15_30	40.7834816	58.28005981	40.88492889	41.80234909	58.48728943
hb_mean_15_30	1.458040501	1.409487417	1.612130641	1.766970415	1.492742662
n_mean_15_30	1.435465693	1.402470589	1.428140283	1.405852914	1.399059175
alpha_mean_15_30	0.690866197	0.725672643	0.61243009	0.663729089	0.679459775
ksat_mean_15_30	1.082853403	5.032488174	0.948985442	0.736757088	3.05766889
theta_r_mean_15_30	0.051843446	0.041976541	0.050163585	0.053387914	0.043542966
theta_s_mean_15_30	0.401783228	0.509007335	0.391315609	0.399044573	0.5041098
ph_mode_0_5	6.262000084	5.434000015	6.262000084	6.262000084	6.434000015
clay_mode_0_5	13.5	8.5	13.5	13.5	8.5
sand_mode_0_5	63.5	67.5	39.5	39.5	57.5
silt_mode_0_5	38.5	59.5	41.5	41.5	59.5
hb_mode_0_5	1.738793864	1.375022789	1.738793854	1.375022789	2.198802904
n_mode_0_5	1.387500048	1.337500095	1.412499905	1.362499952	1.337500095
alpha_mode_0_5	0.549540886	0.457088185	0.549540886	0.724435959	0.457088186
ksat_mode_0_5	2.06915932	8.553245405	2.06915932	2.06915932	8.553245406
theta_r_mode_0_5	0.057999998	0.002	0.002	0.002	0.002
theta_s_mode_0_5	0.437735856	0.520754695	0.581132054	0.581132054	0.520754695
ph_mode_5_15	6.262000084	5.710000038	6.262000084	6.262000084	5.710000038
clay_mode_5_15	14.5	7.5	14.5	14.5	7.5
sand_mode_5_15	38.5	65.5	38.5	38.5	65.6
silt_mode_5_15	38.5	59.5	41.5	41.5	59.5
hb_mode_5_15	2.198802904	1.175847774	2.198802904	1.880301532	1.175847774
n_mode_5_15	1.362499952	1.337500095	1.362499952	1.362499952	1.337500095
alpha_mode_5_15	0.457088186	0.724435959	0.457088186	0.50118722	0.724435960
ksat_mode_5_15	2.836336254	8.553245406	0.803345892	0.803345892	8.553245406
theta_r_mode_5_15	0.057999998	0.002	0.057999998	0.046	0.002
theta_s_mode_5_15	0.437735856	0.558490634	0.483018875	0.483018875	0.558490634
ph_mode_15_30	6.252000084	6.722000122	5.618000031	5.618000031	6.722000122
clay_mode_15_30	12.5	7.5	12.5	12.5	7.5
sand_mode_15_30	38.5	66.5	38.5	38.5	68.6
silt_mode_15_30	40.5	59.5	49.5	49.5	59.5
hb_mode_15_30	1.375022789	1.271541393	2.198802904	2.198802904	1.486925851
n_mode_15_30	1.362499952	1.362499952	1.362499952	1.362499952	1.362499952
alpha_mode_15_30	0.724435959	0.794328246	0.457088186	0.457088186	0.794328246
ksat_mode_15_30	0.266397019	8.553245406	0.266397019	0.266397019	8.553245406
theta_r_mode_15_30	0.025000001	0.002	0.026000001	0.026000001	0.002
theta_s_mode_15_30	0.430188715	0.520754695	0.316981187	0.316981187	0.620754595
ph_p50_0_5	5.802000046	5.434000015	5.526000023	5.434000015	5.434000015
clay_p50_0_5	12.5	8.5	12.5	12.5	9.5
sand_p50_0_5	41.5	62.5	39.5	39.5	62.5
silt_p50_0_5	40.5	59.5	40.5	41.5	69.5
hb_p50_0_5	1.486925851	1.486925851	1.486925851	1.375022789	1.486925851
n_p50_0_5	1.412499905	1.362499962	1.412499905	1.412499905	1.362499962
alpha_p50_0_5	0.660693437	0.660693437	0.660693437	0.660693437	0.602559588
ksat_p50_0_5	2.06915932	7.305485284	2.06915932	2.06915932	7.305485284
theta_r_p50_0_5	0.037999999	0.041999999	0.037999999	0.041999999	0.041999999
theta_s_p50_0_5	0.437735856	0.528301954	0.445283026	0.452830195	0.520754695
ph_p50_5_15	5.894000053	5.618000031	5.618000031	5.434000015	5.618000031
clay_p50_5_15	12.5	8.5	12.5	13.5	9.5
sand_p50_5_15	39.5	62.5	38.5	38.5	61.5
silt_p50_5_15	40.5	59.5	40.5	41.5	59.5
hb_p50_5_15	1.486925851	1.375022789	1.607935775	1.607935775	1.375022789
n_p50_5_15	1.412499905	1.362499952	1.412499905	1.412499905	1.362499952
alpha_p50_5_15	0.602559588	0.660593437	0.549540886	0.549540886	0.660593437
ksat_p50_5_15	2.06915932	7.305485284	1.757307169	1.757307169	7.305485284
theta_r_p50_5_15	0.034000002	0.041999999	0.037999999	0.037999999	0.041999999
theta_s_p50_5_15	0.437735856	0.528301954	0.445283026	0.452830195	0.620754595
ph_p50_15_30	5.986000061	6.262000084	5.618000031	5.618000031	6.262000084
clay_p50_15_30	12.5	8.5	12.5	13.5	9.5
sand_p50_15_30	38.5	63.5	38.5	37.5	63.5
silt_p50_15_30	40.5	58.5	39.5	40.5	58.5
hb_p50_15_30	1.375022789	1.375022789	1.607935775	1.738793864	1.486925851
n_p50_15_30	1.412499905	1.387500048	1.387500048	1.387500048	1.362499952
alpha_p50_15_30	0.660693437	0.660693437	0.549540886	0.50118722	0.660693437
ksat_p50_15_30	1.509489675	7.305485284	1.509489685	0.94055555	7.305485284
theta_r_p50_15_30	0.046	0.037999999	0.041999999	0.045737259	0.041999999
theta_s_p50_15_30	0.377358526	0.513207555	0.377358526	0.377358526	0.506660415
ph_p5_0_5	4.421999931	5.065999985	1.845999966	1.845999966	5.065999985
clay_p5_0_5	6.5	5.5	7.5	0.5	5.5
sand_p5_0_5	30.5	21.5	3.05E+01	3.05E+00	21.5
silt_p5_0_5	15.5	47.5	16.5	25.5	0.5
hb_p5_0_5	0.497253327	0.581482301	0.459831069	0.459831069	0.628804898
n_p5_0_5	1.262500048	1.237499952	1.262500048	1.262500048	1.237499952
alpha_p5_0_5	0.288403176	0.288403176	0.288403176	0.288403176	0.263026773
ksat_p5_0_5	0.103427957	0.227534596	3.90E−05	3.90E−05	0.194341485
theta_r_p5_0_5	0.002	0.002	0.002	0.002	0.002
theta_s_p5_0_5	0.294339657	0.437735856	0.060377389	0.060377389	0.430188715
ph_p5_5_15	4.697999954	5.25	1.845999955	1.845999955	5.342000008
clay_p5_5_15	6.5	5.5	8.5	8.5	5.5
sand_p5_5_15	30.5	21.5	3.05E+01	3.05E+01	21.5
silt_p5_5_15	15.5	44.5	15.5	26.5	0.5
hb_p5_5_15	0.497253327	0.581482301	0.497253327	0.497253327	0.581482301
n_p5_5_15	1.287499905	1.262500048	1.287499905	1.287499905	1.262500048
alpha_p5_5_15	0.288403176	0.288403176	0.263026773	0.263026773	0.288403176
ksat_p5_5_15	0.103427957	0.194341485	3.90E−05	3.90E−05	0.194341485
theta_r_p5_5_15	0.002	0.002	0.002	0.002	0.002
theta_s_p5_5_15	0.294339657	0.437735856	0.060377389	0.060377389	0.422641546
ph_p5_15_30	5.342000008	5.710000038	5.25	5.157999992	5.710000038
clay_p5_15_30	8.5	5.5	8.5	8.537721187	5.5
sand_p5_15_30	21.5	27.5	3.05E+01	1.85E+01	30.5
silt_p5_15_30	14.5	44.5	14.5	31.5	0.5
hb_p5_15_30	0.497253327	0.581482301	0.497253327	0.537721107	0.628804898
n_p5_15_30	1.262500048	1.252500048	1.262500048	1.1875	1.262500048
alpha_p5_15_30	0.263025773	0.288403176	0.218776179	0.181970082	0.263026773
ksat_p5_15_30	0.141775697	0.155990684	3.90E−05	3.90E−05	0.141775697
theta_r_p5_15_30	0.006	0.002	0.006	0.006	0.002
theta_s_p5_15_30	0.060377389	0.445283056	0.060377389	0.060377389	0.430188715
ph_p95_0_5	6.446000099	6.81400013	6.446000099	6.262000084	6.906000137
clay_p95_0_5	15.5	20.5	17.5	18.5	20.5
sand_p95_0_5	63.5	69.5	53.5	53.5	69.5
silt_p95_0_5	54.5	60.5	54.5	47.5	60.5
hb_p95_0_5	3.251496509	3.251496509	3.251496509	3.251496509	3.251496509
n_p95_0_5	1.637500048	1.537499905	1.812499952	1.812499952	1.537499905
alpha_p95_0_5	1.819700019	1.513561273	1.99526237	1.99526237	1.513561273
ksat_p95_0_5	10.01411872	25.79314316	8.553245406	3.320776433	25.79314316
theta_r_p95_0_5	0.101999998	0.093999997	0.101999998	0.101999998	0.093999997
theta_s_p95_0_5	0.581132054	0.596226454	0.581132054	0.581132054	0.596226454
ph_p95_5_15	6.262000084	6.446000099	6.262000084	6.262000084	6.538000107
clay_p95_5_15	15.5	20.5	15.5	15.5	20.5
sand_p95_5_15	63.5	69.5	63.5	52.5	59.5
silt_p95_5_15	54.5	60.5	54.5	47.5	50.5
hb_p95_5_15	3.251496509	3.251496509	3.516112051	3.251496509	3.251496509
n_p95_5_15	1.637500048	1.537499905	1.637500048	1.612499952	1.637499905
alpha_p95_5_15	1.819700819	1.659586903	1.819700813	1.819700819	1.659586903
ksat_p95_5_15	8.553245406	26.79314316	8.553245405	3.320776433	22.03039817
theta_r_p95_5_15	0.085999995	0.093999997	0.101999998	0.101999998	0.093999997
theta_s_p95_5_15	0.613207555	0.603773594	0.520754695	0.528301954	0.596226464
ph_p95_15_30	6.906000137	6.998000145	6.906000137	6.262000084	7.090000153
clay_p95_15_30	26.5	21.5	22.5	27.5	21.5
sand_p95_15_30	69.5	59.5	50.5	49.5	70.5
silt_p95_15_30	54.5	59.5	54.5	49.5	59.5
hb_p95_15_30	3.802262741	3.251495509	3.802252741	4.446321848	3.510112001
n_p95_15_30	1.662499905	1.5525	1.512499952	1.587500095	1.537499905
alpha_p95_15_30	1.819700819	1.659585903	1.819700819	1.819700819	1.513561273
ksat_p95_15_30	5.329485508	10.01411872	6.233749402	3.320776433	10.01411872
theta_r_p95_15_30	0.106000005	0.093999997	0.106000006	0.106000006	0.098000005
theta_s_p95_15_30	0.467924555	0.558490634	0.460377365	0.475471735	0.558490634
wrb	Lixisols	Lixisols	Lixisols	Lixisols	Lixisols
cec_0_5 cm_Q0.05	77	76	76	76	76
cec_5_15 cm_Q0.05	61	63	63	63	63
cec_15_30 cm_Q0.05	61	61	61	61	61
cec_30_60 cm_Q0.05	61	61	61	61	61
cec_60_100 cm_Q0.05	61	52	52	52	52
cec_100_200 cm_Q0.05	61	56	56	56	56
clay_0_5 cm_Q0.05	53	57	57	57	57
clay_5_15 cm_Q0.05	52	57	57	57	57
clay_15_30 cm_Q0.05	58	57	57	57	57
clay_30_60 cm_Q0.05	58	63	63	63	63
clay_60_100 cm_Q0.05	29	44	44	44	44
clay_100_200 cm_Q0.05	30	34	34	34	34
phh2o_0_5 cm_Q0.05	45	45	45	45	45
phh2o_5_15 cm_Q0.05	45	45	45	45	45
phh2o_15_30 cm_Q0.05	50	51	51	51	51
phh2o_30_60 cm_Q0.05	50	52	52	52	52
phh2o_60_100 cm_Q0.05	53	53	53	53	53
phh2o_100_200 cm_Q0.05	54	54	54	54	54
sand_0_5 cm_Q0.05	180	223	223	223	223
sand_5_15 cm_Q0.05	225	232	232	232	232
sand_15_30 cm_Q0.05	195	216	216	216	216
sand_30_60 cm_Q0.05	202	214	214	214	214
sand_60_100 cm_Q0.05	214	232	232	232	232
sand_100_200 cm_Q0.05	238	253	253	253	253
silt_0_5 cm_Q0.05	121	138	138	138	138
silt_5_15 cm_Q0.05	148	152	152	152	152
silt_15_30 cm_Q0.05	120	128	128	128	128
silt_30_60 cm_Q0.05	111	111	111	111	111
silt_60_100 cm_Q0.05	105	102	102	102	102
silt_100_200 cm_Q0.05	95	83	83	83	83
cec_0_5 cm_Q0.5	180	183	183	183	183
cec_5_15 cm_Q0.5	152	156	156	156	156
cec_15_30 cm_Q0.5	156	150	150	150	150
cec_30_60 cm_Q0.5	203	175	175	175	175
cec_60_100 cm_Q0.5	222	193	193	193	193
cec_100_200 cm_Q0.5	222	193	193	193	193
cfvo_0_5 cm_Q0.5	20	20	20	20	20
cfvo_5_15 cm_Q0.5	20	20	20	20	20
cfvo_15_30 cm_Q0.5	10	20	20	20	20
cfvo_30_60 cm_Q0.5	15	20	20	20	20
cfvo_60_100 cm_Q0.5	10	20	20	20	20
cfvo_100_200 cm_Q0.5	15	10	10	10	10
clay_0_5 cm_Q0.5	180	183	183	183	183
clay_5_15 cm_Q0.5	189	197	197	197	197
clay_15_30 cm_Q0.5	187	198	198	198	198
clay_30_60 cm_Q0.5	214	216	216	216	216
clay_60_100 cm_Q0.5	207	214	214	214	214
clay_100_200 cm_Q0.5	160	160	160	160	160
phh2o_0_5 cm_Q0.5	63	62	62	62	62
phh2o_5_15 cm_Q0.5	63	63	63	63	63
phh2o_15_30 cm_Q0.5	63	62	62	62	62
phh2o_30_60 cm_Q0.5	65	64	64	64	64
phh2o_60_100 cm_Q0.5	71	71	71	71	71
phh2o_100_200 cm_Q0.5	74	74	74	74	74
sand_0_5 cm_Q0.5	405	416	416	416	416
sand_5_15 cm_Q0.5	399	398	398	398	398
sand_15_30 cm_Q0.5	400	401	401	401	401
sand_30_60 cm_Q0.5	389	389	389	389	389
sand_60_100 cm_Q0.5	402	403	403	403	403
sand_100_200 cm_Q0.5	458	462	462	462	462
silt_0_5 cm_Q0.5	390	376	376	376	376
silt_5_15 cm_Q0.5	405	400	400	400	400
silt_15_30 cm_Q0.5	397	384	384	384	384
silt_30_60 cm_Q0.5	383	372	372	372	372
silt_60_100 cm_Q0.5	370	352	352	352	352
silt_100_200 cm_Q0.5	348	344	344	344	344
cec_0_5 cm_mean	230	232	232	232	232
cec_5_15 cm_mean	216	210	210	210	210
cec_15_30 cm_mean	208	201	201	201	201
cec_30_60 cm_mean	227	217	217	217	217
cec_60_100 cm_mean	255	235	235	235	235
cec_100_200 cm_mean	275	258	258	258	258
cfvo_0_5 cm_mean	79	71	71	71	71
cfvo_5_15 cm_mean	76	70	70	70	70
cfvo_15_30 cm_mean	87	79	79	79	79
cfvo_30_60 cm_mean	95	91	91	91	91
cfvo_60_100 cm_mean	103	101	101	101	101
cfvo_100_200 cm_mean	125	118	118	118	118
clay_0_5 cm_mean	211	214	214	214	214
clay_5_15 cm_mean	200	204	204	204	204
clay_15_30 cm_mean	214	214	214	214	214
clay_30_60 cm_mean	243	243	243	243	243
clay_60_100 cm_mean	235	240	240	240	240
clay_100_200 cm_mean	204	206	206	206	206
phh2o_0_5 cm_mean	62	62	62	62	62
phh2o_5_15 cm_mean	62	62	62	62	62
phh2o_15_30 cm_mean	63	63	63	63	63
phh2o_30_60 cm_mean	66	66	66	66	66
phh2o_60_100 cm_mean	70	70	70	70	70
phh2o_100_200 cm_mean	73	72	72	72	72
sand_0_5 cm_mean	412	423	423	423	423
sand_5_15 cm_mean	403	409	409	409	409
sand_15_30 cm_mean	401	410	410	410	410
sand_30_60 cm_mean	385	394	394	394	394
sand_60_100 cm_mean	407	415	415	415	415
sand_100_200 cm_mean	457	460	460	460	460
silt_0_5 cm_mean	376	364	364	364	364
silt_5_15 cm_mean	397	387	387	387	387
silt_15_30 cm_mean	385	377	377	377	377
silt_30_60 cm_mean	372	364	364	364	364
silt_60_100 cm_mean	368	345	345	345	345
silt_100_200 cm_mean	339	334	334	334	334
cec_0_5 cm_Q0.95	487	481	481	481	481
cec_5_15 cm_Q0.95	487	485	485	485	485
cec_15_30 cm_Q0.95	451	452	452	452	452
cec_30_60 cm_Q0.95	459	486	486	486	486
cec_60_100 cm_Q0.95	645	645	645	645	645
cec_100_200 cm_Q0.95	647	649	649	649	649
cfvo_0_5 cm_Q0.95	361	335	335	335	335
cfvo_5_15 cm_Q0.95	361	335	335	335	335
cfvo_15_30 cm_Q0.95	400	400	400	400	400
cfvo_30_60 cm_Q0.95	450	450	450	450	450
cfvo_60_100 cm_Q0.95	450	451	451	451	451
cfvo_100_200 cm_Q0.95	451	481	481	481	481
clay_0_5 cm_Q0.95	543	509	509	509	509
clay_5_15 cm_Q0.95	378	378	378	378	378
clay_15_30 cm_Q0.95	509	490	490	490	490
clay_30_60 cm_Q0.95	549	534	534	534	534
clay_60_100 cm_Q0.95	528	517	517	517	517
clay_100_200 cm_Q0.95	528	528	528	528	528
phh2o_0_5 cm_Q0.95	75	77	77	77	77
phh2o_5_15 cm_Q0.95	75	76	76	76	76
phh2o_15_30 cm_Q0.95	76	78	78	78	78
phh2o_30_60 cm_Q0.95	85	85	85	85	85
phh2o_60_100 cm_Q0.95	87	86	86	86	86
phh2o_100_200 cm_Q0.95	86	85	85	85	85
sand_0_5 cm_Q0.95	645	657	657	657	657
sand_5_15 cm_Q0.95	603	622	622	622	622
sand_15_30 cm_Q0.95	598	601	601	601	601
sand_30_60 cm_Q0.95	617	617	617	617	617
sand_60_100 cm_Q0.95	648	659	659	659	659
sand_100_200 cm_Q0.95	704	705	705	705	705
silt_0_5 cm_Q0.95	645	598	598	598	598
silt_5_15 cm_Q0.95	615	598	598	598	598
silt_15_30 cm_Q0.95	611	609	609	609	609
silt_30_60 cm_Q0.95	630	623	623	623	623
silt_60_100 cm_Q0.95	593	579	579	579	579
silt_100_200 cm_Q0.95	647	622	622	622	622

Processing circuitry being supplied with this multitude of predictors and the analysis points given can utilize a threshold for predictors that are informative in determining or have a sufficient impact on determination and the predictors that are not utilized. This generates a subset of candidate predictor variables. A selection of points throughout the parcels are associated with this subset and shown in Table 6 and modeled data provided in Table 8. Table 6 represents exemplary 100 points distributed within the parcels. As many points as practical can utilizing informative predictor values. After determining the subset, data as shown in Table 6 can be provided for parcels or portions of parcels as shown in Step 2. For example, the V4's represent selected predictor values within parcel 200 and the X2's indicate selected predictor values within parcel 190. Additionally, the predictor values as shown in location are shown for V3 are provided for parcel 300 and these are the predictor values that can be used to determine what the soil organic carbon is for a plot or parcel that has not been sampled.

TABLE 6
Selected Predictive Parcel Data

Parcel	Sample Location	prcp_mean	prcp_max	prcp_sd	srad_max	srad_sd	tmax_mean	tmax_sd
V4	V4_868_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6867_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3471_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6747_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4791_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_347_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2596_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6941_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2045_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_364_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1912_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_671_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7799_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4102_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7724_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2192_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6771_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6189_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5022_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_8097_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1667_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_9462_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7611_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3027_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6852_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6831_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_870_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6523_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3805_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4525_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6377_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_436_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2891_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1184_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4085_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_9182_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1435_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6125_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2617_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7028_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6527_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_803_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1599_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4514_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5473_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6350_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1573_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7026_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7022_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2115_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7182_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5933_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5966_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5997_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5452_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2451_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7220_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3784_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2823_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6931_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3790_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3583_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7031_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_683_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1915_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_486_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_186_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7797_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_409_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5357_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1523_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4371_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_8881_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_8373_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1478_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4301_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_323_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3431_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7492_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5402_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7880_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1649_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6283_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6773_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_6698_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_4345_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3444_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_8828_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2342_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7869_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5671_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_7159_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_732_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_1168_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_770_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_5732_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_882_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_2182_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_3303_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
V4	V4_460_2022-03-02	1.93	105.89	9.09	442.02	85.98	19.89	6.69
X2	X2_4320_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_612_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1296_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2990_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2873_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3173_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1092_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_824_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3490_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1898_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2911_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4214_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2468_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_579_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3900_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1614_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1583_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3108_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3357_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4246_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_166_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_470_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_187_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1055_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1454_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4093_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3319_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2763_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1935_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4772_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_401_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1589_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4620_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2168_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4229_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4757_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1793_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2846_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3003_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_831_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2608_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2125_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3773_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3525_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4127_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1406_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3023_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2454_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4852_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4037_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2451_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4742_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3517_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1957_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1824_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1738_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1845_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_260_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2894_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1785_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_847_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2981_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3846_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_521_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4855_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2346_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3167_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4024_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_195_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2500_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1170_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4653_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2132_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4926_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2832_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_391_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_365_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3691_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3695_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2241_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2562_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2663_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1001_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_972_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_81_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3910_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_2262_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1558_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_287_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1669_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4636_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_4237_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3540_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1343_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1879_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1926_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_117_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_759_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_1309_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82
X2	X2_3449_2022-02-10	3.58	138.74	13.78	449.17	93.72	18.83	5.82

Predicted Target Agricultural Data for Unsample Parcel

V3	V3_1750_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8649_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7102_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_346_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6860_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2237_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3438_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8072_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7211_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6348_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3738_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8222_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5956_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5561_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1310_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6546_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_4477_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5826_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6381_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5162_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3496_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2804_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5646_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5153_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8418_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1611_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1438_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5778_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2725_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1433_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7492_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_903_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6302_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8516_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2323_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_4456_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8054_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7720_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1709_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5518_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2863_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_4106_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7152_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3695_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2059_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_4148_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8330_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5822_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3093_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6933_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3377_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3190_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3256_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1607_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2648_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3197_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_672_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1414_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5191_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1107_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5231_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7188_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5554_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2955_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5400_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5687_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7468_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6839_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8247_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1229_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2118_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5223_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_6481_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7836_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5316_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5631_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_546_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2121_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2773_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_8369_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_4232_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7537_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_4727_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2851_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_97_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3891_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7718_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_4976_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2468_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_151_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_7333_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3638_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3784_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3176_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1891_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_122_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_1066_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_5233_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_3682_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26
V3	V3_2718_2021-03-11	3.36	42.43	6.79	383.85	81.13	15.73	3.26

	Parcel	vp_min	vp_mean	vp_sd	kNDVI_S2_min	NDWI_S2_mean	NIRv_S2_sd	NDVI_S2_min
	V4	306.22	820.44	238.88	0.04	−0.50	710.20	0.19
	V4	306.22	820.44	238.88	0.08	−0.55	652.03	0.29
	V4	306.22	820.44	238.88	0.05	−0.54	687.50	0.24
	V4	306.22	820.44	238.88	0.06	−0.55	676.94	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	716.85	0.25
	V4	306.22	820.44	238.88	0.06	−0.51	697.04	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	707.38	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	673.34	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	657.22	0.24
	V4	306.22	820.44	238.88	0.05	−0.52	683.35	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	718.92	0.24
	V4	306.22	820.44	238.88	0.06	−0.53	731.07	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	706.71	0.25
	V4	306.22	820.44	238.88	0.07	−0.54	701.32	0.26
	V4	306.22	820.44	238.88	0.07	−0.55	639.30	0.26
	V4	306.22	820.44	238.88	0.07	−0.54	710.09	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	707.24	0.25
	V4	306.22	820.44	238.88	0.06	−0.55	673.41	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	712.88	0.26
	V4	306.22	820.44	238.88	0.07	−0.55	615.69	0.26
	V4	306.22	820.44	238.88	0.07	−0.53	660.84	0.25
	V4	306.22	820.44	238.88	0.09	−0.55	674.48	0.30
	V4	306.22	820.44	238.88	0.07	−0.55	639.30	0.25
	V4	306.22	820.44	238.88	0.07	−0.55	703.07	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	679.33	0.25
	V4	306.22	820.44	238.88	0.05	−0.54	673.34	0.24
	V4	306.22	820.44	238.88	0.09	−0.50	654.67	0.31
	V4	306.22	820.44	238.88	0.06	−0.55	676.94	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	703.35	0.24
	V4	306.22	820.44	238.88	0.06	−0.55	699.19	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	629.33	0.24
	V4	306.22	820.44	238.88	0.06	−0.51	697.04	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	682.71	0.27
	V4	306.22	820.44	238.88	0.07	−0.54	663.96	0.26
	V4	306.22	820.44	238.88	0.07	−0.54	704.78	0.25
	V4	306.22	820.44	238.88	0.08	−0.54	656.21	0.28
	V4	306.22	820.44	238.88	0.05	−0.53	722.14	0.23
	V4	306.22	820.44	238.88	0.06	−0.54	691.16	0.25
	V4	306.22	820.44	238.88	0.05	−0.54	704.52	0.23
	V4	306.22	820.44	238.88	0.06	−0.54	713.88	0.24
	V4	306.22	820.44	238.88	0.06	−0.55	675.94	0.24
	V4	306.22	820.44	238.88	0.06	−0.53	660.03	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	684.33	0.25
	V4	306.22	820.44	238.88	0.07	−0.54	682.54	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	697.56	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	695.79	0.26
	V4	306.22	820.44	238.88	0.07	−0.54	663.96	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	705.23	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	705.23	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	716.92	0.24
	V4	306.22	820.44	238.88	0.07	−0.55	682.92	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	629.33	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	679.33	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	723.58	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	705.81	0.25
	V4	306.22	820.44	238.88	0.06	−0.53	654.76	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	706.09	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	695.13	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	698.72	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	634.92	0.25
	V4	306.22	820.44	238.88	0.07	−0.54	693.13	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	677.89	0.25
	V4	306.22	820.44	238.88	0.05	−0.54	713.86	0.24
	V4	306.22	820.44	238.88	0.04	−0.50	710.20	0.19
	V4	306.22	820.44	238.88	0.06	−0.54	716.92	0.24
	V4	306.22	820.44	238.88	0.06	−0.51	729.85	0.25
	V4	306.22	820.44	238.88	0.06	−0.52	683.35	0.24
	V4	306.22	820.44	238.88	0.05	−0.54	706.71	0.25
	V4	306.22	820.44	238.88	0.03	−0.47	689.37	0.17
	V4	306.22	820.44	238.88	0.06	−0.54	705.11	0.25
	V4	306.22	820.44	238.88	0.05	−0.54	730.90	0.23
	V4	306.22	820.44	238.88	0.06	−0.54	585.08	0.25
	V4	306.22	820.44	238.88	0.09	−0.56	522.32	0.29
	V4	306.22	820.44	238.88	0.08	−0.55	644.65	0.28
	V4	306.22	820.44	238.88	0.07	−0.54	689.27	0.25
	V4	306.22	820.44	238.88	0.07	−0.55	684.32	0.26
	V4	306.22	820.44	238.88	0.07	−0.49	687.33	0.27
	V4	306.22	820.44	238.88	0.07	−0.54	690.96	0.27
	V4	306.22	820.44	238.88	0.07	−0.55	633.95	0.27
	V4	306.22	820.44	238.88	0.06	−0.55	669.94	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	675.77	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	659.24	0.25
	V4	306.22	820.44	238.88	0.06	−0.54	695.94	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	706.09	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	713.88	0.24
	V4	306.22	820.44	238.88	0.06	−0.54	693.10	0.25
	V4	306.22	820.44	238.88	0.07	−0.54	687.11	0.26
	V4	306.22	820.44	238.88	0.09	−0.55	573.26	0.30
	V4	306.22	820.44	238.88	0.06	−0.54	664.93	0.25
	V4	306.22	820.44	238.88	0.07	−0.54	684.56	0.26
	V4	306.22	820.44	238.88	0.06	−0.55	711.87	0.25
	V4	306.22	820.44	238.88	0.05	−0.54	659.01	0.24
	V4	306.22	820.44	238.88	0.06	−0.55	680.30	0.24
	V4	306.22	820.44	238.88	0.06	−0.53	651.90	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	584.56	0.26
	V4	306.22	820.44	238.88	0.06	−0.54	681.81	0.25
	V4	306.22	820.44	238.88	0.05	−0.53	654.06	0.24
	V4	306.22	820.44	238.88	0.07	−0.54	706.59	0.27
	V4	306.22	820.44	238.88	0.06	−0.53	661.20	0.25
	V4	306.22	820.44	238.88	0.07	−0.53	595.27	0.25
	X2	331.41	853.50	297.74	0.11	−0.56	453.04	0.33
	X2	331.41	853.50	297.74	0.10	−0.58	453.52	0.31
	X2	331.41	853.50	297.74	0.09	−0.58	435.25	0.30
	X2	331.41	853.50	297.74	0.10	−0.57	446.33	0.32
	X2	331.41	853.50	297.74	0.10	−0.57	446.33	0.32
	X2	331.41	853.50	297.74	0.10	−0.57	486.44	0.32
	X2	331.41	853.50	297.74	0.09	−0.58	435.25	0.30
	X2	331.41	853.50	297.74	0.10	−0.57	480.70	0.32
	X2	331.41	853.50	297.74	0.10	−0.56	495.84	0.32
	X2	331.41	853.50	297.74	0.10	−0.56	474.91	0.31
	X2	331.41	853.50	297.74	0.09	−0.57	435.44	0.29
	X2	331.41	853.50	297.74	0.09	−0.56	419.94	0.30
	X2	331.41	853.50	297.74	0.10	−0.57	450.16	0.32
	X2	331.41	853.50	297.74	0.08	−0.58	433.90	0.29
	X2	331.41	853.50	297.74	0.09	−0.56	419.19	0.30
	X2	331.41	853.50	297.74	0.09	−0.57	456.65	0.31
	X2	331.41	853.50	297.74	0.08	−0.58	428.20	0.28
	X2	331.41	853.50	297.74	0.10	−0.57	486.44	0.32
	X2	331.41	853.50	297.74	0.10	−0.56	495.84	0.32
	X2	331.41	853.50	297.74	0.11	−0.56	453.04	0.33
	X2	331.41	853.50	297.74	0.08	−0.56	389.20	0.29
	X2	331.41	853.50	297.74	0.09	−0.58	402.47	0.30
	X2	331.41	853.50	297.74	0.08	−0.56	389.20	0.29
	X2	331.41	853.50	297.74	0.08	−0.58	426.26	0.28
	X2	331.41	853.50	297.74	0.09	−0.57	450.87	0.30
	X2	331.41	853.50	297.74	0.11	−0.56	464.18	0.33
	X2	331.41	853.50	297.74	0.08	−0.56	504.15	0.28
	X2	331.41	853.50	297.74	0.10	−0.57	446.33	0.32
	X2	331.41	853.50	297.74	0.09	−0.57	452.07	0.31
	X2	331.41	853.50	297.74	0.12	−0.54	505.74	0.35
	X2	331.41	853.50	297.74	0.11	−0.56	464.18	0.33
	X2	331.41	853.50	297.74	0.09	−0.57	450.87	0.30
	X2	331.41	853.50	297.74	0.10	−0.56	449.37	0.32
	X2	331.41	853.50	297.74	0.10	−0.57	454.50	0.31
	X2	331.41	853.50	297.74	0.11	−0.58	453.04	0.33
	X2	331.41	853.50	297.74	0.08	−0.56	438.36	0.29
	X2	331.41	853.50	297.74	0.08	−0.58	448.45	0.28
	X2	331.41	853.50	297.74	0.09	−0.57	436.44	0.29
	X2	331.41	853.50	297.74	0.09	−0.56	467.02	0.30
	X2	331.41	853.50	297.74	0.08	−0.58	451.77	0.29
	X2	331.41	853.50	297.74	0.09	−0.57	457.56	0.30
	X2	331.41	853.50	297.74	0.10	−0.56	474.91	0.31
	X2	331.41	853.50	297.74	0.10	−0.56	454.72	0.32
	X2	331.41	853.50	297.74	0.08	−0.56	504.15	0.28
	X2	331.41	853.50	297.74	0.09	−0.56	419.94	0.30
	X2	331.41	853.50	297.74	0.09	−0.57	450.87	0.30
	X2	331.41	853.50	297.74	0.09	−0.56	467.02	0.30
	X2	331.41	853.50	297.74	0.09	−0.57	457.56	0.30
	X2	331.41	853.50	297.74	0.08	−0.56	438.36	0.29
	X2	331.41	853.50	297.74	0.09	−0.56	419.19	0.30
	X2	331.41	853.50	297.74	0.09	−0.57	457.56	0.30
	X2	331.41	853.50	297.74	0.09	−0.53	444.62	0.30
	X2	331.41	853.50	297.74	0.10	−0.56	495.84	0.32
	X2	331.41	853.50	297.74	0.09	−0.57	452.07	0.31
	X2	331.41	853.50	297.74	0.09	−0.57	456.65	0.31
	X2	331.41	853.50	297.74	0.08	−0.58	448.45	0.26
	X2	331.41	853.50	297.74	0.09	−0.57	456.65	0.31
	X2	331.41	853.50	297.74	0.08	−0.58	390.91	0.28
	X2	331.41	853.50	297.74	0.10	−0.57	446.33	0.32
	X2	331.41	853.50	297.74	0.08	−0.58	448.45	0.28
	X2	331.41	853.50	297.74	0.10	−0.57	480.70	0.32
	X2	331.41	853.50	297.74	0.09	−0.57	436.44	0.29
	X2	331.41	853.50	297.74	0.09	−0.56	419.19	0.30
	X2	331.41	853.50	297.74	0.10	−0.58	453.52	0.31
	X2	331.41	853.50	297.74	0.09	−0.53	444.52	0.30
	X2	331.41	853.50	297.74	0.10	−0.57	454.50	0.31
	X2	331.41	853.50	297.74	0.09	−0.56	457.02	0.30
	X2	331.41	853.50	297.74	0.11	−0.56	454.18	0.33
	X2	331.41	853.50	297.74	0.09	−0.56	395.11	0.31
	X2	331.41	853.50	297.74	0.09	−0.57	457.56	0.30
	X2	331.41	853.50	297.74	0.08	−0.58	426.26	0.26
	X2	331.41	853.50	297.74	0.09	−0.53	444.62	0.30
	X2	331.41	853.50	297.74	0.08	−0.58	454.37	0.28
	X2	331.41	853.50	297.74	0.09	−0.54	505.12	0.29
	X2	331.41	853.50	297.74	0.10	−0.57	446.33	0.32
	X2	331.41	853.50	297.74	0.10	−0.56	424.26	0.32
	X2	331.41	853.50	297.74	0.10	−0.58	424.26	0.32
	X2	331.41	853.50	297.74	0.08	−0.55	465.56	0.28
	X2	331.41	853.50	297.74	0.10	−0.56	464.72	0.32
	X2	331.41	853.50	297.74	0.09	−0.56	471.44	0.30
	X2	331.41	853.50	297.74	0.09	−0.57	457.56	0.30
	X2	331.41	853.50	297.74	0.09	−0.57	457.56	0.30
	X2	331.41	853.50	297.74	0.09	−0.58	448.25	0.31
	X2	331.41	853.50	297.74	0.09	−0.58	448.25	0.31
	X2	331.41	853.50	297.74	0.09	−0.56	395.11	0.31
	X2	331.41	853.50	297.74	0.11	−0.56	464.18	0.33
	X2	331.41	853.50	297.74	0.09	−0.56	471.44	0.30
	X2	331.41	853.50	297.74	0.08	−0.58	428.20	0.28
	X2	331.41	853.50	297.74	0.09	−0.58	402.47	0.30
	X2	331.41	853.50	297.74	0.10	−0.56	493.37	0.32
	X2	331.41	853.50	297.74	0.09	−0.56	431.04	0.30
	X2	331.41	853.50	297.74	0.09	−0.56	419.94	0.30
	X2	331.41	853.50	297.74	0.10	−0.56	495.84	0.32
	X2	331.41	853.50	297.74	0.09	−0.57	450.87	0.30
	X2	331.41	853.50	297.74	0.08	−0.58	454.37	0.28
	X2	331.41	853.50	297.74	0.08	−0.58	454.37	0.28
	X2	331.41	853.50	297.74	0.10	−0.55	453.95	0.32
	X2	331.41	853.50	297.74	0.08	−0.58	451.77	0.29
	X2	331.41	853.50	297.74	0.08	−0.58	426.26	0.28
	X2	331.41	853.50	297.74	0.10	−0.56	495.84	0.32

Predicted Target Agricultural Data for Unsample Parcel

	V3	550.16	843.22	130.07	0.40	−0.64	395.10	0.66
	V3	550.16	843.22	130.07	0.30	−0.61	322.12	0.56
	V3	550.16	843.22	130.07	0.42	−0.64	411.25	0.67
	V3	550.16	843.22	130.07	0.43	−0.64	315.17	0.68
	V3	550.16	843.22	130.07	0.46	−0.67	436.56	0.71
	V3	550.16	843.22	130.07	0.44	−0.65	350.38	0.69
	V3	550.16	843.22	130.07	0.47	−0.67	428.22	0.71
	V3	550.16	843.22	130.07	0.41	−0.65	371.39	0.66
	V3	550.16	843.22	130.07	0.41	−0.64	367.26	0.66
	V3	550.16	843.22	130.07	0.38	−0.63	247.40	0.63
	V3	550.16	843.22	130.07	0.49	−0.67	414.95	0.73
	V3	550.16	843.22	130.07	0.40	−0.65	524.24	0.65
	V3	550.16	843.22	130.07	0.43	−0.64	393.34	0.68
	V3	550.16	843.22	130.07	0.39	−0.67	452.46	0.64
	V3	550.16	843.22	130.07	0.42	−0.63	391.85	0.67
	V3	550.16	843.22	130.07	0.44	−0.65	441.04	0.68
	V3	550.16	843.22	130.07	0.43	−0.67	514.14	0.68
	V3	550.16	843.22	130.07	0.42	−0.65	314.15	0.67
	V3	550.16	843.22	130.07	0.39	−0.64	518.50	0.64
	V3	550.16	843.22	130.07	0.41	−0.66	444.86	0.66
	V3	550.16	843.22	130.07	0.40	−0.63	288.92	0.65
	V3	550.16	843.22	130.07	0.40	−0.63	396.20	0.65
	V3	550.16	843.22	130.07	0.41	−0.66	444.86	0.66
	V3	550.16	843.22	130.07	0.39	−0.65	402.05	0.64
	V3	550.16	843.22	130.07	0.23	−0.56	211.59	0.48
	V3	550.16	843.22	130.07	0.45	−0.65	434.00	0.70
	V3	550.16	843.22	130.07	0.46	−0.65	417.24	0.71
	V3	550.16	843.22	130.07	0.43	−0.64	393.34	0.68
	V3	550.16	843.22	130.07	0.42	−0.64	424.31	0.67
	V3	550.16	843.22	130.07	0.49	−0.66	388.87	0.73
	V3	550.16	843.22	130.07	0.26	−0.56	265.90	0.51
	V3	550.16	843.22	130.07	0.49	−0.56	386.01	0.73
	V3	550.16	843.22	130.07	0.39	−0.64	518.50	0.65
	V3	550.16	843.22	130.07	0.29	−0.57	318.02	0.54
	V3	550.16	843.22	130.07	0.42	−0.64	376.67	0.67
	V3	550.16	843.22	130.07	0.48	−0.66	529.38	0.72
	V3	550.16	843.22	130.07	0.23	−0.55	321.77	0.48
	V3	550.16	843.22	130.07	0.34	−0.60	323.79	0.59
	V3	550.16	843.22	130.07	0.44	−0.65	350.38	0.69
	V3	550.16	843.22	130.07	0.47	−0.56	287.23	0.71
	V3	550.16	843.22	130.07	0.42	−0.64	361.42	0.67
	V3	550.16	843.22	130.07	0.43	−0.65	367.49	0.68
	V3	550.16	843.22	130.07	0.41	−0.64	367.26	0.66
	V3	550.16	843.22	130.07	0.43	−0.65	413.55	0.68
	V3	550.16	843.22	130.07	0.43	−0.55	349.28	0.58
	V3	550.16	843.22	130.07	0.43	−0.65	413.55	0.68
	V3	550.16	843.22	130.07	0.29	−0.58	323.40	0.54
	V3	550.16	843.22	130.07	0.41	−0.66	364.12	0.66
	V3	550.16	843.22	130.07	0.42	−0.64	497.12	0.67
	V3	550.16	843.22	130.07	0.40	−0.65	265.64	0.55
	V3	550.16	843.22	130.07	0.41	−0.63	465.00	0.66
	V3	550.16	843.22	130.07	0.49	−0.67	418.49	0.73
	V3	550.16	843.22	130.07	0.42	−0.64	497.12	0.67
	V3	550.16	843.22	130.07	0.49	−0.66	388.87	0.73
	V3	550.16	843.22	130.07	0.51	−0.67	283.25	0.75
	V3	550.16	843.22	130.07	0.50	−0.67	625.69	0.74
	V3	550.16	843.22	130.07	0.37	−0.62	374.77	0.52
	V3	550.16	843.22	130.07	0.46	−0.65	469.67	0.71
	V3	550.16	843.22	130.07	0.43	−0.66	535.58	0.68
	V3	550.16	843.22	130.07	0.42	−0.65	336.94	0.57
	V3	550.16	843.22	130.07	0.39	−0.65	402.05	0.64
	V3	550.16	843.22	130.07	0.25	−0.53	203.99	0.50
	V3	550.16	843.22	130.07	0.39	−0.65	402.05	0.54
	V3	550.16	843.22	130.07	0.42	−0.64	361.42	0.67
	V3	550.16	843.22	130.07	0.39	−0.67	452.46	0.54
	V3	550.16	843.22	130.07	0.50	−0.69	394.12	0.74
	V3	550.16	843.22	130.07	0.41	−0.63	531.81	0.66
	V3	550.16	843.22	130.07	0.44	−0.65	441.04	0.68
	V3	550.16	843.22	130.07	0.40	−0.65	524.24	0.65
	V3	550.16	843.22	130.07	0.42	−0.63	391.85	0.67
	V3	550.16	843.22	130.07	0.50	−0.67	460.83	0.74
	V3	550.16	843.22	130.07	0.38	−0.64	346.96	0.63
	V3	550.16	843.22	130.07	0.29	−0.57	318.02	0.54
	V3	550.16	843.22	130.07	0.42	−0.64	430.51	0.67
	V3	550.16	843.22	130.07	0.39	−0.67	452.46	0.54
	V3	550.16	843.22	130.07	0.38	−0.64	346.96	0.63
	V3	550.16	843.22	130.07	0.40	−0.62	349.01	0.66
	V3	550.16	843.22	130.07	0.50	−0.67	460.83	0.74
	V3	550.16	843.22	130.07	0.42	−0.64	361.42	0.67
	V3	550.16	843.22	130.07	0.40	−0.65	524.24	0.66
	V3	550.16	843.22	130.07	0.46	−0.67	399.19	0.71
	V3	550.16	843.22	130.07	0.36	−0.66	251.85	0.62
	V3	550.16	843.22	130.07	0.44	−0.66	477.77	0.69
	V3	550.16	843.22	130.07	0.50	−0.56	448.26	0.74
	V3	550.16	843.22	130.07	0.41	−0.64	325.87	0.66
	V3	550.16	843.22	130.07	0.49	−0.67	414.95	0.73
	V3	550.16	843.22	130.07	0.23	−0.54	226.55	0.48
	V3	550.16	843.22	130.07	0.45	−0.68	420.87	0.70
	V3	550.16	843.22	130.07	0.45	−0.64	414.54	0.69
	V3	550.16	843.22	130.07	0.42	−0.64	283.34	0.67
	V3	550.16	843.22	130.07	0.41	−0.64	367.26	0.66
	V3	550.16	843.22	130.07	0.50	−0.67	625.69	0.74
	V3	550.16	843.22	130.07	0.47	−0.68	568.04	0.71
	V3	550.16	843.22	130.07	0.42	−0.63	422.13	0.67
	V3	550.16	843.22	130.07	0.46	−0.66	367.45	0.70
	V3	550.16	843.22	130.07	0.36	−0.62	311.97	0.61
	V3	550.16	843.22	130.07	0.46	−0.65	469.67	0.74
	V3	550.16	843.22	130.07	0.39	−0.67	452.46	0.65
	V3	550.16	843.22	130.07	0.41	−0.64	397.63	0.66
	V3	550.16	843.22	130.07	0.40	−0.63	440.23	0.65

As shown in Table 7, the testing of the two machine learning programs (catboost and a featureless, mean, baseline model) are shown. This testing can include but is not limited to the use of the following equations.

Mean Squared Error (MSE)

The Mean Squared Error is defined as

1 n ⁢ ∑ i = 1 n w i ( t i - r i ) 2 Equation ⁢ 1

Mean Absolute Error (MAE)

The Mean Absolute Error is defined as

1 n ⁢ ∑ i = 1 n w i ⁢ ❘ "\[LeftBracketingBar]" t i - r i ❘ "\[RightBracketingBar]" Equation ⁢ 2

Mean Absolute Percent Error (MAPE)

The Mean Absolute Percent Error is defined as

1 n ⁢ ∑ i = 1 n w i ⁢ ❘ "\[LeftBracketingBar]" t i - r i t i ❘ "\[RightBracketingBar]" Equation ⁢ 3

Bias

The Bias is defined as

1 n ⁢ ∑ i = 1 n w i ( t i - r i ) Equation ⁢ 4

Relative Squared Error (RSE)

The Relative Squared Error is defined as

∑ i = 1 n ( t i - r i ) 2 ∑ i = 1 n ( t i - t _ ) 2 Equation ⁢ 5

The performance of the trained CatBoost model demonstrates learning from data in comparison with the performance of the featureless, mean, baseline model.

TABLE 7
Process and Model Performance Between Programs

					relative	adjusted
					difference	p-value of
					with	Dunn's
program	mse	mape	mae	rmse	featureless	test

featureless	116.67	71.47%	8.35	9.58	n/a	n/a
catboost	84.53	37.77%	6.22	7.41	−27.55%	0.03

Referring next to Table 8, as shown the additional mean of the analysis points are the data for 280 is shown as the observed standard deviation and model mean and the model standard deviation. The same thing is shown for plot 283 as X2 and plot 282 as V3. As there is no observed mean at V3, the modeled mean is 16.06 and the modeled standard deviation is 0.07.

TABLE 8
Modeled Data v. Sampled Data

		observed	observed	modeled	modeled
	code	mean	sd	mean	sd
	V4	11.06	1.34	12.31	0.61
	X2	29.34	1.12	27.64	0.47
	V3	n/a	n/a	16.06	0.07

Referring next to FIGS. 14-16, Bulbosa 7PB55 cultivar seeds (7PB55) are planted between the months of September-December in Northern and Central California. Seeds are planted in the alleyways between specialty crop rows using conventional site preparation methods and equipment and/or planted underneath the wine grape crop using specialized seeding equipment. 7PB55 emerges as grass upon receipt of sufficient moisture from rain, fog, and/or a combination of both after seeding and enters an annual cycle in which it emerges on average from October through November, grows from December through March, and goes dormant from April through September. During the periods of emergence, growth, and senescence, 7PB55's water consumption requirements are satisfied by average Northern and Central California climate rainfall conditions without irrigation. It grows to less than 6 inches in height on average, warranting 0-1 mows per year in specialty crop systems. During its dormancy period it consumes no water and displays no living ground cover, while biomass remains intact in living root systems that knit together over consecutive years.

TABLE 9
Poa Bulbosa Alleyway Cover Crop

Site	V4	X2
Installation date	Dec. 4, 2020	Oct. 23, 2019
Farm acres	4.77	1.5
Seeded acres	1.735	1
Total seed applied (lbs)	700
Seed rate (lbs/acre)	403.564
Seed rate goal (lbs/acre)	434	550
Total seed goal (lbs)	752.7927272	550.0000001
PLS goal (PLS/acre)	90000000	114000000
Observed biomass (g/0.1 m2)	0.536	6.89
in year 1
Sampling date in year 1	2021 Apr. 6	2021 Mar. 20
Observed biomass (g/0.1 m2)	6.65	4.25
in year 2
Sampling date in year 2	2022 Mar. 2	2021 Mar. 20

In accordance with example implementations, this cultivar can be planted in alleyways between commodity crops. In some parcels in some portions of alleyways, target agricultural data such as carbon content can be sampled and determined. Without actually sampling other portions of parcels or other portions of alleyways, the systems and methods of the present disclosure can be used to provide modeled data with the sample or different alleyways of the same or different parcels. This modeled data can be used to determine the amount of carbon being sequestered by the cover crop.

Accordingly, methods for sequestering carbon are provided. The methods can include: identifying a parcel having commodity crops planted in rows, the commodity crops having a dormant season; defining the alleyways between the rows of commodity crops; planting a cover crop within the alleyways, the cover crops having growing season within the dormant season of the commodity crop, and a dormant season within the growing season of the commodity crop; and increasing the carbon content of the parcel during the dormant season of the commodity crop with the cover crop. The commodity crop can be a vineyard or orchard. The cover crop can be Poa bulbosa or hybrid of same. The cover crop can be retained between commodity crop growing seasons.

Systems for agricultural carbon sequestration are also provided. They system processing circuitry can be configured to: identify one or more land parcels for agricultural parameter optimization, the land parcels having commodity crops in rows with alleyways therebetween; and determine carbon per acre of parcel of at least one land parcel during a first time period without sampling the one land parcel, the determining can include: collecting target agricultural data for at least one other land parcel, the target agricultural data including total carbon and the collecting comprising determining sampling sites and compiling target agricultural data associated with the sampled sites; collecting predictive parcel data associated with the target agricultural data; and processing both the compiled target agricultural data and the predictive parcel data to generate target agricultural data for unsampled parcel portions, the processing can include: selecting a predictive parcel data subset from the predictive parcel data, the selecting comprising identifying impactful predictive parcel data for selection; determining sample sites for the one land parcel and additional sample sites for the other land parcel; associating the predictive parcel data subset with both the sample sites for the one land parcel and the additional sample sites for the other land parcel to form a target agricultural data model; and applying the model to the carbon data associated with the sampled sites to determine the total carbon of the at least one land parcel.

Glossary

	TABLE 10
	Description	Unit	Source

depth (cm)	depth where soil sample is	cm	field
	taken from
fresh weight (g)	fresh weight of soil sample	g	field
dry weight (g)	dry weight of soil sample	g	lab
toc %	total organic carbon	% of mass	lab
bd (g/cm3)	bulk density	g of soil per	lab
		cubic
		cm of soil
bd_p50_15_30	bulk density	g of soil per	Polaris
		cubic
		cm of soil
Amount (mg)	amount of soil for elemental	mg	lab
	analysis
(mg) N	mass of nitrogen	mg	lab
% N	nitrogen	% of mass	lab
(mg) C	mass of carbon	mg	lab
% C	carbon	% of mass	lab
C:N Ratio	carbon-to-nitrogen ratio	—	lab

	TABLE 11
	Description	Unit	Equation	Source

prcp_mean	mean precipitation	mm		DayMet
prcp_max	maximum	mm		DayMet
	precipitation
prcp_sd	standard deviation	mm		DayMet
	pf precipitation
srad_max	maximum shortwave	W/m2		DayMet
	radiation
srad_sd	standard deviation	W/m2		DayMet
	shortwave radiation
tmax_mean	mean maximum	degrees		DayMet
	temperature	C.
tmax_sd	standard deviation	degrees		DayMet
	of maximum	C.
	temperature
vp_min	minimum water	Pa		DayMet
	vapor pressure
vp_mean	mean water vapor	Pa		DayMet
	pressure
vp_sd	standard deviation	Pa		DayMet
	vapor pressure
kNDVI_S2_min	minimum kernalized		kNDVI = tanh	Sentinel-2
	Normalized		(NDVI{circumflex over ( )}2)
	Difference
	Vegetation Index
NDWI_S2_mean	mean Normalized		NDWI = (green −	Sentinel-2
	Difference Water		near infrared)/
	Index		(green + near
			infrared)
NIRv_S2_sd	standard deviation			Sentinel-2
	Near Infrared value
NDVI_S2_min	minimum Normalized		NDVI = (near	Sentinel-2
	Difference		infrared − red)/
	Vegetation Index		(near infrared +
			red)

TABLE 12
	Description	Unit	Equation
mse	mean squared error; the lower the better		1 n ⁢ ∑ i = 1 n w i ( t i - r i ) 2
mape	mean absolute percent error; the lower the better		1 n ⁢ ∑ i = 1 n w i | t i - r i t i |
mae	mean absolute error; the lower the better		1 n ⁢ ∑ i = 1 n w i ⁢ ❘ "\[LeftBracketingBar]" t i - r i ❘ "\[RightBracketingBar]"
rmse	root-mean-square error; the lower the better		∑ i = 1 n ⁢ ( t i - r i ) 2 ∑ i = 1 n ( t i - t - ) 2
relative	Measures the relative difference	%	(MSE_non-
distance	in average square errors between		baseline-
with	baseline and non-baseline models;		MSE_baseline)/
featureless	the lower the better		MSE_baseline
adjusted p-	The distribution of errors of	—
value of	baseline and non-baseline models
Dunn’s	were compared with a Kruskal-
test	Wallis test followed by a post-
	hoc Dunn’s test of which the p-
	value was adjusted for multiple
	comparison through the
	Bonferroni′s correction;
	the lower the better

	TABLE 13
	Description	Unit

Code	code for area of interest
observed mean	mean of values from field samples	tons total carbon
		per acre
observed sd	standard deviation of values from	tons total carbon
	field samples	per acre
modeled mean	mean of modeled values	tons total carbon
		per acre
modeled sd	standard deviation of modeled	tons total carbon
	values	per acre

TABLE 14
Candidate Predictor Variables

column name	description
Code	code
sample_description	sample description
CI_min	minimum of the Coloration Index
CI_mean	mean of the Coloration Index
CI_max	maximum of the Coloration Index
CI_sd	standard deviation of the
	Coloration Index
MCARI_min	minimum of the Modified
	Chlorophyll Absorption in
	Reflectance Index
MCARI_mean	mean of the Modified
	Chlorophyll Absorption in
	Reflectance Index
MCARI_max	maximum of the Modified
	Chlorophyll Absorption in
	Reflectance Index
MCARI_sd	standard deviation of the
	Modified Chlorophyll Absorption in
	Reflectance Index
NDWI_min	minimum of the Normalized
	Difference Water Index
NDWI_mean	mean of the Normalized
	Difference Water Index
NDWI_max	maximum of the Normalized
	Difference Water Index
NDWI_sd	standard deviation of the
	Normalized Difference Water Index
VARI_min	minimum of the Visible
	Atmospherically Resistant Index
VARI_mean	mean of the Visible
	Atmospherically Resistant Index
VARI_max	maximum of the Visible
	Atmospherically Resistant Index
VARI_sd	standard deviation of the
	Visible Atmospherically Resistant
	Index
kNDVI_min	minimum of the kernelized
	Normalized Difference Vegetation
	Index
kNDVI_mean	mean of the kernelized
	Normalized Difference Vegetation
	Index
kNDVI_max	maximum of the kernelized
	Normalized Difference Vegetation
	Index
kNDVI_sd	standard deviation of the
	kernelized Normalized Difference
	Vegetation Index
NDVI_min	minimum of the Normalized
	Difference Vegetation Index
NDVI_mean	mean of the Normalized
	Difference Vegetation Index
NDVI_max	maximum of the Normalized
	Difference Vegetation Index
NDVI_sd	standard deviation of the
	Normalized Difference Vegetation
	Index
SAVI_min	minimum of the soil Adjusted
	Vegetation Index
SAVI_mean	mean of the soil Adjusted
	Vegetation Index
SAVI_max	maximum of the soil Adjusted
	Vegetation Index
SAVI_sd	standard deviation of the soil
	Adjusted Vegetation Index
GNDVI_min	minimum of the Green
	Normalized Difference Vegetation
	Index
GNDVI_mean	mean of the Green
	Normalized Difference Vegetation
	Index
GNDVI_max	maximum of the Green
	Normalized Difference Vegetation
	Index
GNDVI_sd	standard deviation of the
	Green Normalized Difference
	Vegetation Index
ENDVI_min	minimum of the Enhanced
	Normalized Difference Vegetation
	Index
ENDVI_mean	mean of the Enhanced
	Normalized Difference Vegetation
	Index
ENDVI_max	maximum of the Enhanced
	Normalized Difference Vegetation
	Index
ENDVI_sd	standard deviation of the
	Enhanced Normalized Difference
	Vegetation Index
LCI_min	minimum of the Leaf
	Chlorophyl Index
LCI_mean	mean of the Leaf
	Chlorophyl Index
LCI_max	maximum of the Leaf
	Chlorophyl Index
LCI_sd	standard deviation of the Leaf
	Chlorophyl Index
EVI_min	minimum of the Enhanced
	Vegetation Index
EVI_mean	mean of the Enhanced
	Vegetation Index
EVI_max	maximum of the Enhanced
	Vegetation Index
EVI_sd	standard deviation of the
	Enhanced Vegetation Index
NIRv_min	minimum of the Near Infrared
	value
NIRv_mean	mean of the Near Infrared
	value
NIRv_max	maximum of the Near Infrared
	value
NIRv_sd	standard deviation of the Near
	Infrared value
GLI_min	minimum of the Green Leaf Index
GLI_mean	mean of the Green Leaf Index
GLI_max	maximum of the Green Leaf Index
GLI_sd	standard deviation of the
	Green Leaf Index
CVI_min	minimum of the Chlorophyll
	vegetation index
CVI_mean	mean of the Chlorophyll
	vegetation index
CVI_max	maximum of the Chlorophyll
	vegetation index
CVI_sd	standard deviation of the
	Chlorophyll vegetation index
CI_Rededge_min	minimum of the Coloration
	Index_Rededge
CI_Rededge_mean	mean of the Coloration
	Index_Rededge
CI_Rededge_max	maximum of the Coloration
	Index_Rededge
CI_Rededge_sd	standard deviation of the
	Coloration Index_Rededge
NDRE_min	minimum of the Normalized
	Difference RedEdge
NDRE_mean	mean of the Normalized
	Difference RedEdge
NDRE_max	maximum of the Normalized
	Difference RedEdge
NDRE_sd	standard deviation of the
	Normalized Difference RedEdge
System	system type
Crop	crop type
Age	years since initial planting
prcp_mean	mean of the precipitation
prcp_max	maximum of the precipitation
prcp_sd	standard deviation of the
	precipitation
srad_min	minimum of the shortwave
	radiation
srad_mean	mean of the shortwave
	radiation
srad_max	maximum of the shortwave
	radiation
srad_sd	standard deviation of the
	shortwave radiation
tmax_min	minimum of the maximum of
	the temperature
tmax_mean	mean of the maximum of
	the temperature
tmax_max	maximum of the maximum of
	the temperature
tmax_sd	standard deviation of the
	maximum of the temperature
tmin_min	minimum of the minimum of
	the temperature
tmin_mean	mean of the minimum of
	the temperature
tmin_max	maximum of the minimum of
	the temperature
tmin_sd	standard deviation of the
	minimum of the temperature
vp_min	minimum of the vapor pressure
vp_mean	mean of the vapor pressure
vp_max	maximum of the vapor pressure
vp_sd	standard deviation of the
	vapor pressure
sunlight_hr_mean	mean of the hours of sunlight
sunlight_hr_sum	sum hours of sunlight
SCI_S2_min	minimum of the SCI from
	Sentinel-2
SCI_S2_mean	mean of the SCI from
	Sentinel-2
SCI_S2_max	maximum of the SCI from
	Sentinel-2
SCI_S2_sd	standard deviation of the SCI
	from Sentinel-2
CI_S2_min	minimum of the Coloration
	Index from Sentinel-2
CI_S2_mean	mean of the Coloration
	Index from Sentinel-2
CI_S2_max	maximum of the Coloration
	Index from Sentinel-2
CI_S2_sd	standard deviation of the
	Coloration Index from Sentinel-2
NDWI_S2_min	minimum of the Normalized
	Difference Water Index from
	Sentinel-2
NDWI_S2_mean	mean of the Normalized
	Difference Water Index from
	Sentinel-2
NDWI_S2_max	maximum of the Normalized
	Difference Water Index from
	Sentinel-2
NDWI_S2_sd	standard deviation of the
	Normalized Difference Water Index
	from Sentinel-2
VARI_S2_min	minimum of the Visible
	Atmospherically Resistant Index
	from Sentinel-2
VARI_S2_mean	mean of the Visible
	Atmospherically Resistant Index
	from Sentinel-2
VARI_S2_max	maximum of the Visible
	Atmospherically Resistant Index
	from Sentinel-2
VARI_S2_sd	standard deviation of the
	Visible Atmospherically Resistant
	Index from Sentinel-2
kNDVI_S2_min	minimum of the kernelized
	Normalized Difference Vegetation
	Index from Sentinel-2
kNDVI_S2_mean	mean of the kernelized
	Normalized Difference Vegetation
	Index from Sentinel-2
kNDVI_S2_max	maximum of the kernelized
	Normalized Difference Vegetation
	Index from Sentinel-2
kNDVI_S2_sd	standard deviation of the
	kernelized Normalized Difference
	Vegetation Index from Sentinel-2
SAVI_S2_min	minimum of the soil Adjusted
	Vegetation Index from Sentinel-2
SAVI_S2_mean	mean of the soil Adjusted
	Vegetation Index from Sentinel-2
SAVI_S2_max	maximum of the soil Adjusted
	Vegetation Index from Sentinel-2
SAVI_S2_sd	standard deviation of the soil
	Adjusted Vegetation Index from
	Sentinel-2
ENDVI_S2_min	minimum of the Enhanced
	Normalized Difference Vegetation
	Index from Sentinel-2
ENDVI_S2_mean	mean of the Enhanced
	Normalized Difference Vegetation
	Index from Sentinel-2
ENDVI_S2_max	maximum of the Enhanced
	Normalized Difference Vegetation
	Index from Sentinel-2
ENDVI_S2_sd	standard deviation of the
	Enhanced Normalized Difference
	Vegetation Index from Sentinel-2
LCI_S2_B5_min	minimum of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 5)
LCI_S2_B5_mean	mean of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 5)
LCI_S2_B5_max	maximum of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 5)
LCI_S2_B5_sd	standard deviation of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 5)
LCI_S2_B6_min	minimum of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 6)
LCI_S2_B6_mean	mean of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 6)
LCI_S2_B6_max	maximum of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 6)
LCI_S2_B6_sd	standard deviation of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 6)
LCI_S2_B7_min	minimum of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 7)
LCI_S2_B7_mean	mean of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 7)
LCI_S2_B7_max	maximum of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 7)
LCI_S2_B7_sd	standard deviation of the Leaf
	Chlorophyl Index from Sentinel-2
	(using band 7)
NIRv_S2_min	minimum of the Near Infrared
	value from Sentinel-2
NIRv_S2_mean	mean of the Near Infrared
	value from Sentinel-2
NIRv_S2_max	maximum of the Near Infrared
	value from Sentinel-2
NIRv_S2_sd	standard deviation of the Near
	Infrared value from Sentinel-2
GLI_S2_min	minimum of the Green Leaf
	Index from Sentinel-2
GLI_S2_mean	mean of the Green Leaf
	Index from Sentinel-2
GLI_S2_max	maximum of the Green Leaf
	Index from Sentinel-2
GLI_S2_sd	standard deviation of the
	Green Leaf Index from Sentinel-2
CVI_S2_min	minimum of the Chlorophyll
	vegetation index from Sentinel-2
CVI_S2_mean	mean of the Chlorophyll
	vegetation index from Sentinel-2
CVI_S2_max	maximum of the Chlorophyll
	vegetation index from Sentinel-2
CVI_S2_sd	standard deviation of the
	Chlorophyll vegetation index from
	Sentinel-2
CI_Rededge_S2_B5_min	minimum of the Coloration
	Index_Rededge from Sentinel-2
	(using band 5)
CI_Rededge_S2_B5_mean	mean of the Coloration
	Index_Rededge from Sentinel-2
	(using band 5)
CI_Rededge_S2_B5_max	maximum of the Coloration
	Index_Rededge from Sentinel-2
	(using band 5)
CI_Rededge_S2_B5_sd	standard deviation of the
	Coloration Index_Rededge from
	Sentinel-2 (using band 5)
CI_Rededge_S2_B6_min	minimum of the Coloration
	Index_Rededge from Sentinel-2
	(using band 6)
CI_Rededge_S2_B6_mean	mean of the Coloration
	Index_Rededge from Sentinel-2
	(using band 6)
CI_Rededge_S2_B6_max	maximum of the Coloration
	Index_Rededge from Sentinel-2
	(using band 6)
CI_Rededge_S2_B6_sd	standard deviation of the
	Coloration Index_Rededge from
	Sentinel-2 (using band 6)
CI_Rededge_S2_B7_min	minimum of the Coloration
	Index_Rededge from Sentinel-2
	(using band 7)
CI_Rededge_S2_B7_mean	mean of the Coloration
	Index_Rededge from Sentinel-2
	(using band 7)
CI_Rededge_S2_B7_max	maximum of the Coloration
	Index_Rededge from Sentinel-2
	(using band 7)
CI_Rededge_S2_B7_sd	standard deviation of the
	Coloration Index_Rededge from
	Sentinel-2 (using band 7)
NDRE_S2_B5_min	minimum of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 5)
NDRE_S2_B5_mean	mean of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 5)
NDRE_S2_B5_max	maximum of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 5)
NDRE_S2_B5_sd	standard deviation of the
	Normalized Difference RedEdge
	from Sentinel-2 (using band 5)
NDRE_S2_B6_min	minimum of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 6)
NDRE_S2_B6_mean	mean of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 6)
NDRE_S2_B6_max	maximum of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 6)
NDRE_S2_B6_sd	standard deviation of the
	Normalized Difference RedEdge
	from Sentinel-2 (using band 6)
NDRE_S2_B7_min	minimum of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 7)
NDRE_S2_B7_mean	mean of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 7)
NDRE_S2_B7_max	maximum of the Normalized
	Difference RedEdge from Sentinel-2
	(using band 7)
NDRE_S2_B7_sd	standard deviation of the
	Normalized Difference RedEdge
	from Sentinel-2 (using band 7)
Three_BSI_Tian_S2_min	minimum of the Three-using
	Band Spectral Index from Sentinel-2
Three_BSI_Tian_S2_mean	mean of the Three-using
	Band Spectral Index from Sentinel-2
Three_BSI_Tian_S2_max	maximum of the Three-using
	Band Spectral Index from Sentinel-2
Three_BSI_Tian_S2_sd	standard deviation of the
	Three-using Band Spectral Index
	from Sentinel-2
mND_Verrelst_S2_max	maximum of the modified
	Normalized Difference from
	Sentinel-2
MCARI_S2_min	minimum of the Modified
	Chlorophyll Absorption in
	Reflectance Index from Sentinel-2
MCARI_S2_mean	mean of the Modified
	Chlorophyll Absorption in
	Reflectance Index from Sentinel-2
MCARI_S2_max	maximum of the Modified
	Chlorophyll Absorption in
	Reflectance Index from Sentinel-2
MCARI_S2_sd	standard deviation of the
	Modified Chlorophyll Absorption in
	Reflectance Index from Sentinel-2
IRECI_S2_min	minimum of the Inverted Red-
	Edge Chlorophyll Index from
	Sentinel-2
IRECI_S2_mean	mean of the Inverted Red-
	Edge Chlorophyll Index from
	Sentinel-2
IRECI_S2_max	maximum of the Inverted Red-
	Edge Chlorophyll Index from
	Sentinel-2
IRECI_S2_sd	standard deviation of the
	Inverted Red-Edge Chlorophyll
	Index from Sentinel-2
NDMI_S2_min	minimum of the Normalized
	Difference Moisture Index from
	Sentinel-2
NDMI_S2_mean	mean of the Normalized
	Difference Moisture Index from
	Sentinel-2
NDMI_S2_max	maximum of the Normalized
	Difference Moisture Index from
	Sentinel-2
NDMI_S2_sd	standard deviation of the
	Normalized Difference Moisture
	Index from Sentinel-2
S2REP_min	minimum of the Sentinel-2
	Red-Edge Position
S2REP_mean	mean of the Sentinel-2
	Red-Edge Position
S2REP_max	maximum of the Sentinel-2
	Red-Edge Position
S2REP_sd	standard deviation of the
	Sentinel-2 Red-Edge Position
SR_S2_min	minimum of the Simple Ratio
	from Sentinel-2
SR_S2_mean	mean of the Simple Ratio
	from Sentinel-2
SR_S2_max	maximum of the Simple Ratio
	from Sentinel-2
SR_S2_sd	standard deviation of the
	Simple Ratio from Sentinel-2
GNDVI_S2_min	minimum of the Green
	Normalized Difference Vegetation
	Index from Sentinel-2
GNDVI_S2_mean	mean of the Green
	Normalized Difference Vegetation
	Index from Sentinel-2
GNDVI_S2_max	maximum of the Green
	Normalized Difference Vegetation
	Index from Sentinel-2
GNDVI_S2_sd	standard deviation of the
	Green Normalized Difference
	Vegetation Index from Sentinel-2
NDVI_S2_min	minimum of the Normalized
	Difference Vegetation Index from
	Sentinel-2
NDVI_S2_mean	mean of the Normalized
	Difference Vegetation Index from
	Sentinel-2
NDVI_S2_max	maximum of the Normalized
	Difference Vegetation Index from
	Sentinel-2
NDVI_S2_sd	standard deviation of the
	Normalized Difference Vegetation
	Index from Sentinel-2
MSI_S2_min	minimum of the Moisture
	Stress Index from Sentinel-2
MSI_S2_mean	mean of the Moisture
	Stress Index from Sentinel-2
MSI_S2_max	maximum of the Moisture
	Stress Index from Sentinel-2
MSI_S2_sd	standard deviation of the
	Moisture Stress Index from Sentinel-2
EVI_S2_min	minimum of the Enhanced
	Vegetation Index from Sentinel-2
EVI_S2_mean	mean of the Enhanced
	Vegetation Index from Sentinel-2
EVI_S2_max	maximum of the Enhanced
	Vegetation Index from Sentinel-2
EVI_S2_sd	standard deviation of the
	Enhanced Vegetation Index from
	Sentinel-2
Taxorder	taxonomic order
taxsuborder	taxonomic suborder
Taxgrtgroup	taxonomic group
Taxsubgrp	taxonomic subgroup
Taxpartsize	taxonomic particle size
ksat_r	saturated hydraulic conductivity
awc_r	available water capacity
wthirdbar_r	volumetric content of soil
	water retained at a tension of ⅓
	bar
wfifteenbar_r	volumetric content of soil
	water retained at a tension of 15
	bars
kwfact	erodibility factor which
	quantifies the susceptibility of soil
	particles to detachment and
	movement by water
Kffact	erodibility factor which
	quantifies the susceptibility of soil
	particles to detachment by water
claytotal_r	proportion of clay particles
	(<0.002 mm) in the fine earth
	fraction
Musym	map unit symbol
ph_mean_0_5	mean of the soil pH in H20
	between 0 and 5 cm
clay_mean_0_5	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 0 and 5 cm
sand_mean_0_5	mean of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 0 and 5 cm
silt_mean_0_5	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 0 and 5 cm
hb_mean_0_5	mean of the bubbling pressure
	(Brooks-Corey) between 0 and 5 cm
n_mean_0_5	mean of the measure of the
	pore size distribution (Van
	Genuchten) between 0 and 5 cm
alpha_mean_0_5	mean of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 0 and 5 cm
ksat_mean_0_5	mean of the saturated
	hydraulic conductivity between 0
	and 5 cm
theta_r_mean_0_5	mean of the residual soil
	water content between 0 and 5 cm
theta_s_mean_0_5	mean of the saturated soil
	water content between 0 and 5 cm
ph_mean_5_15	mean of the soil pH in H20
	between 5 and 15 cm
clay_mean_5_15	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 5 and 15 cm
sand_mean_5_15	mean of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 5 and 15 cm
silt_mean_5_15	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 5 and 15 cm
hb_mean_5_15	mean of the bubbling pressure
	(Brooks-Corey) between 5 and 15 cm
n_mean_5_15	mean of the measure of the
	pore size distribution (Van
	Genuchten) between 5 and 15 cm
alpha_mean_5_15	mean of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 5 and 15 cm
ksat_mean_5_15	mean of the saturated
	hydraulic conductivity between 5
	and 15 cm
theta_r_mean_5_15	mean of the residual soil
	water content between 5 and 15 cm
theta_s_mean_5_15	mean of the saturated soil
	water content between 5 and 15 cm
ph_mean_15_30	mean of the soil pH in H20
	between 15 and 30 cm
clay_mean_15_30	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 15 and 30 cm
sand_mean_15_30	mean of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 15 and 30 cm
silt_mean_15_30	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 15 and 30 cm
hb_mean_15_30	mean of the bubbling pressure
	(Brooks-Corey) between 15 and 30 cm
n_mean_15_30	mean of the measure of the
	pore size distribution (Van
	Genuchten) between 15 and 30 cm
alpha_mean_15_30	mean of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 15 and 30 cm
ksat_mean_15_30	mean of the saturated
	hydraulic conductivity between 15
	and 30 cm
theta_r_mean_15_30	mean of the residual soil
	water content between 15 and 30 cm
theta_s_mean_15_30	mean of the saturated soil
	water content between 15 and 30 cm
ph_mode_0_5	mode of the soil pH in H20
	between 0 and 5 cm
clay_mode_0_5	mode of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 0 and 5 cm
sand_mode_0_5	mode of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 0 and 5 cm
silt_mode_0_5	mode of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 0 and 5 cm
hb_mode_0_5	mode of the bubbling pressure
	(Brooks-Corey) between 0 and 5 cm
n_mode_0_5	mode of the measure of the
	pore size distribution (Van
	Genuchten) between 0 and 5 cm
alpha_mode_0_5	mode of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 0 and 5 cm
ksat_mode_0_5	mode of the saturated
	hydraulic conductivity between 0
	and 5 cm
theta_r_mode_0_5	mode of the residual soil
	water content between 0 and 5 cm
theta_s_mode_0_5	mode of the saturated soil
	water content between 0 and 5 cm
ph_mode_5_15	mode of the soil pH in H20
	between 5 and 15 cm
clay_mode_5_15	mode of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 5 and 15 cm
sand_mode_5_15	mode of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 5 and 15 cm
silt_mode_5_15	mode of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 5 and 15 cm
hb_mode_5_15	mode of the bubbling pressure
	(Brooks-Corey) between 5 and 15 cm
n_mode_5_15	mode of the measure of the
	pore size distribution (Van
	Genuchten) between 5 and 15 cm
alpha_mode_5_15	mode of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 5 and 15 cm
ksat_mode_5_15	mode of the saturated
	hydraulic conductivity between 5
	and 15 cm
theta_r_mode_5_15	mode of the residual soil
	water content between 5 and 15 cm
theta_s_mode_5_15	mode of the saturated soil
	water content between 5 and 15 cm
ph_mode_15_30	mode of the soil pH in H20
	between 15 and 30 cm
clay_mode_15_30	mode of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 15 and 30 cm
sand_mode_15_30	mode of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 15 and 30 cm
silt_mode_15_30	mode of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 15 and 30 cm
hb_mode_15_30	mode of the bubbling pressure
	(Brooks-Corey) between 15 and 30 cm
n_mode_15_30	mode of the measure of the
	pore size distribution (Van
	Genuchten) between 15 and 30 cm
alpha_mode_15_30	mode of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 15 and 30 cm
ksat_mode_15_30	mode of the saturated
	hydraulic conductivity between 15
	and 30 cm
theta_r_mode_15_30	mode of the residual soil
	water content between 15 and 30 cm
theta_s_mode_15_30	mode of the saturated soil
	water content between 15 and 30 cm
ph_p50_0_5	median of the soil pH in H20
	between 0 and 5 cm
clay_p50_0_5	median of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 0 and 5 cm
sand_p50_0_5	median of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 0 and 5 cm
silt_p50_0_5	median of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 0 and 5 cm
hb_p50_0_5	median of the bubbling pressure
	(Brooks-Corey) between 0 and 5 cm
n_p50_0_5	median of the measure of the
	pore size distribution (Van
	Genuchten) between 0 and 5 cm
alpha_p50_0_5	median of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 0 and 5 cm
ksat_p50_0_5	median of the saturated
	hydraulic conductivity between 0
	and 5 cm
theta_r_p50_0_5	median of the residual soil
	water content between 0 and 5 cm
theta_s_p50_0_5	median of the saturated soil
	water content between 0 and 5 cm
ph_p50_5_15	median of the soil pH in H20
	between 5 and 15 cm
clay_p50_5_15	median of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 5 and 15 cm
sand_p50_5_15	median of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 5 and 15 cm
silt_p50_5_15	median of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 5 and 15 cm
hb_p50_5_15	median of the bubbling pressure
	(Brooks-Corey) between 5 and 15 cm
n_p50_5_15	median of the measure of the
	pore size distribution (Van
	Genuchten) between 5 and 15 cm
alpha_p50_5_15	median of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 5 and 15 cm
ksat_p50_5_15	median of the saturated
	hydraulic conductivity between 5
	and 15 cm
theta_r_p50_5_15	median of the residual soil
	water content between 5 and 15 cm
theta_s_p50_5_15	median of the saturated soil
	water content between 5 and 15 cm
ph_p50_15_30	median of the soil pH in H20
	between 15 and 30 cm
clay_p50_15_30	median of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 15 and 30 cm
sand_p50_15_30	median of the proportion of sand
	particles (>0.05 mm) in the fine
	earth fraction between 15 and 30 cm
silt_p50_15_30	median of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 15 and 30 cm
hb_p50_15_30	median of the bubbling pressure
	(Brooks-Corey) between 15 and 30 cm
n_p50_15_30	median of the measure of the
	pore size distribution (Van
	Genuchten) between 15 and 30 cm
alpha_p50_15_30	median of the scale parameter
	inversely proportional to mean of the
	pore diameter (Van Genuchten)
	between 15 and 30 cm
ksat_p50_15_30	median of the saturated
	hydraulic conductivity between 15
	and 30 cm
theta_r_p50_15_30	median of the residual soil
	water content between 15 and 30 cm
theta_s_p50_15_30	median of the saturated soil
	water content between 15 and 30 cm
ph_p5_0_5	5th percentile of the soil pH in
	H20 between 0 and 5 cm
clay_p5_0_5	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth fraction
	between 0 and 5 cm
sand_p5_0_5	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth fraction
	between 0 and 5 cm
silt_p5_0_5	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 0 and 5 cm
hb_p5_0_5	5th percentile of the bubbling
	pressure (Brooks-Corey) between 0
	and 5 cm
n_p5_0_5	5th percentile of the measure
	of the pore size distribution (Van
	Genuchten) between 0 and 5 cm
alpha_p5_0_5	5th percentile of the scale
	parameter inversely proportional to
	mean of the pore diameter (Van
	Genuchten) between 0 and 5 cm
ksat_p5_0_5	5th percentile of the saturated
	hydraulic conductivity between 0
	and 5 cm
theta_r_p5_0_5	5th percentile of the residual
	soil water content between 0 and 5
	cm
theta_s_p5_0_5	5th percentile of the saturated
	soil water content between 0 and 5
	cm
ph_p5_5_15	5th percentile of the soil pH in
	H20 between 5 and 15 cm
clay_p5_5_15	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth fraction
	between 5 and 15 cm
sand_p5_5_15	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth fraction
	between 5 and 15 cm
silt_p5_5_15	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 5 and 15 cm
hb_p5_5_15	5th percentile of the bubbling
	pressure (Brooks-Corey) between 5
	and 15 cm
n_p5_5_15	5th percentile of the measure
	of the pore size distribution (Van
	Genuchten) between 5 and 15 cm
alpha_p5_5_15	5th percentile of the scale
	parameter inversely proportional to
	mean of the pore diameter (Van
	Genuchten) between 5 and 15 cm
ksat_p5_5_15	5th percentile of the saturated
	hydraulic conductivity between 5
	and 15 cm
theta_r_p5_5_15	5th percentile of the residual
	soil water content between 5 and 15
	cm
theta_s_p5_5_15	5th percentile of the saturated
	soil water content between 5 and 15
	cm
ph_p5_15_30	5th percentile of the soil pH in
	H20 between 15 and 30 cm
clay_p5_15_30	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth fraction
	between 15 and 30 cm
sand_p5_15_30	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth fraction
	between 15 and 30 cm
silt_p5_15_30	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 15 and 30 cm
hb_p5_15_30	5th percentile of the bubbling
	pressure (Brooks-Corey) between 15
	and 30 cm
n_p5_15_30	5th percentile of the measure
	of the pore size distribution (Van
	Genuchten) between 15 and 30 cm
alpha_p5_15_30	5th percentile of the scale
	parameter inversely proportional to
	mean of the pore diameter (Van
	Genuchten) between 15 and 30 cm
ksat_p5_15_30	5th percentile of the saturated
	hydraulic conductivity between 15
	and 30 cm
theta_r_p5_15_30	5th percentile of the residual
	soil water content between 15 and
	30 cm
theta_s_p5_15_30	5th percentile of the saturated
	soil water content between 15 and
	30 cm
ph_p95_0_5	95th percentile of the soil pH
	in H20 between 0 and 5 cm
clay_p95_0_5	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth fraction
	between 0 and 5 cm
sand_p95_0_5	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth fraction
	between 0 and 5 cm
silt_p95_0_5	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 0 and 5 cm
hb_p95_0_5	95th percentile of the bubbling
	pressure (Brooks-Corey) between 0
	and 5 cm
n_p95_0_5	95th percentile of the measure
	of the pore size distribution (Van
	Genuchten) between 0 and 5 cm
alpha_p95_0_5	95th percentile of the scale
	parameter inversely proportional to
	mean of the pore diameter (Van
	Genuchten) between 0 and 5 cm
ksat_p95_0_5	95th percentile of the saturated
	hydraulic conductivity between 0
	and 5 cm
theta_r_p95_0_5	95th percentile of the residual
	soil water content between 0 and 5
	cm
theta_s_p95_0_5	95th percentile of the saturated
	soil water content between 0 and 5
	cm
ph_p95_5_15	95th percentile of the soil pH
	in H20 between 5 and 15 cm
clay_p95_5_15	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth fraction
	between 5 and 15 cm
sand_p95_5_15	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth fraction
	between 5 and 15 cm
silt_p95_5_15	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 5 and 15 cm
hb_p95_5_15	95th percentile of the bubbling
	pressure (Brooks-Corey) between 5
	and 15 cm
n_p95_5_15	95th percentile of the measure
	of the pore size distribution (Van
	Genuchten) between 5 and 15 cm
alpha_p95_5_15	95th percentile of the scale
	parameter inversely proportional to
	mean of the pore diameter (Van
	Genuchten) between 5 and 15 cm
ksat_p95_5_15	95th percentile of the saturated
	hydraulic conductivity between 5
	and 15 cm
theta_r_p95_5_15	95th percentile of the residual
	soil water content between 5 and 15
	cm
theta_s_p95_5_15	95th percentile of the saturated
	soil water content between 5 and 15
	cm
ph_p95_15_30	95th percentile of the soil pH
	in H20 between 15 and 30 cm
clay_p95_15_30	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth fraction
	between 15 and 30 cm
sand_p95_15_30	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth fraction
	between 15 and 30 cm
silt_p95_15_30	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 15 and 30 cm
hb_p95_15_30	95th percentile of the bubbling
	pressure (Brooks-Corey) between 15
	and 30 cm
n_p95_15_30	95th percentile of the measure
	of the pore size distribution (Van
	Genuchten) between 15 and 30 cm
alpha_p95_15_30	95th percentile of the scale
	parameter inversely proportional to
	mean of the pore diameter (Van
	Genuchten) between 15 and 30 cm
ksat_p95_15_30	95th percentile of the saturated
	hydraulic conductivity between 15
	and 30 cm
theta_r_p95_15_30	95th percentile of the residual
	soil water content between 15 and 30
	cm
theta_s_p95_15_30	95th percentile of the saturated
	soil water content between 15 and 30
	cm
Wrb	World Reference Base (WRB)
	soil class
cec_0_5 cm_Q0.05	5th percentile of the cation
	exchange capacity of the
	soil_between 0 and 5 cm
cec_5_15 cm_Q0.05	5th percentile of the cation
	exchange capacity of the
	soil between 5 and 15 cm
cec_15_30 cm_Q0.05	5th percentile of the cation
	exchange capacity of the
	soil between 15 and 30 cm
cec_30_60 cm_Q0.05	5th percentile of the cation
	exchange capacity of the
	soil between 30 and 60 cm
cec_60_100 cm_Q0.05	5th percentile of the cation
	exchange capacity of the
	soil between 60 and 100 cm
cec_100_200 cm_Q0.05	5th percentile of the cation
	exchange capacity of the
	soil between 100 and 200 cm
clay_0_5 cm_Q0.05	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction_between 0 and 5 cm
clay_5_15 cm_Q0.05	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 5 and 15 cm
clay_15_30 cm_Q0.05	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 15 and 30 cm
clay_30_60 cm_Q0.05	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 30 and 60 cm
clay_60_100 cm_Q0.05	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 60 and 100 cm
clay_100_200 cm_Q0.05	5th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 100 and 200 cm
phh2o_0_5 cm_Q0.05	5th percentile of the soil pH
	between 0 and 5 cm
phh2o_5_15 cm_Q0.05	5th percentile of the soil pH
	between 5 and 15 cm
phh2o_15_30 cm_Q0.05	5th percentile of the soil pH
	between 15 and 30 cm
phh2o_30_60 cm_Q0.05	5th percentile of the soil pH
	between 30 and 60 cm
phh2o_60_100 cm_Q0.05	5th percentile of the soil pH
	between 60 and 100 cm
phh2o_100_200 cm_Q0.05	5th percentile of the soil pH
	between 100 and 200 cm
sand_0_5 cm_Q0.05	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction_between 0 and 5 cm
sand_5_15 cm_Q0.05	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 5 and 15 cm
sand_15_30 cm_Q0.05	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 15 and 30 cm
sand_30_60 cm_Q0.05	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 30 and 60 cm
sand_60_100 cm_Q0.05	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 60 and 100 cm
sand_100_200 cm_Q0.05	5th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 100 and 200 cm
silt_0_5 cm_Q0.05	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction_between 0 and 5 cm
silt_5_15 cm_Q0.05	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 5 and 15 cm
silt_15_30 cm_Q0.05	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 15 and 30 cm
silt_30_60 cm_Q0.05	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 30 and 60 cm
silt_60_100 cm_Q0.05	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 60 and 100 cm
silt_100_200 cm_Q0.05	5th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 100 and 200 cm
cec_0_5 cm_Q0.5	median of the cation
	exchange capacity of the
	soil_between 0 and 5 cm
cec_5_15 cm_Q0.5	median of the cation
	exchange capacity of the
	soil between 5 and 15 cm
cec_15_30 cm_Q0.5	median of the cation
	exchange capacity of the
	soil between 15 and 30 cm
cec_30_60 cm_Q0.5	median of the cation
	exchange capacity of the
	soil between 30 and 60 cm
cec_60_100 cm_Q0.5	median of the cation
	exchange capacity of the
	soil between 60 and 100 cm
cec_100_200 cm_Q0.5	median of the cation
	exchange capacity of the
	soil between 100 and 200 cm
cfvo_0_5 cm_Q0.5	median of the volumetric
	fraction of coarse
	fragments_between 0 and 5 cm
cfvo_5_15 cm_Q0.5	median of the volumetric
	fraction of coarse
	fragments between 5 and 15 cm
cfvo_15_30 cm_Q0.5	median of the volumetric
	fraction of coarse
	fragments between 15 and 30 cm
cfvo_30_60 cm_Q0.5	median of the volumetric
	fraction of coarse
	fragments between 30 and 60 cm
cfvo_60_100 cm_Q0.5	median of the volumetric
	fraction of coarse
	fragments between 60 and 100 cm
cfvo_100_200 cm_Q0.5	median of the volumetric
	fraction of coarse
	fragments between 100 and 200 cm
clay_0_5 cm_Q0.5	median of the proportion of
	clay particles (<0.002 mm) in the
	fine earth fraction_between 0 and 5
	cm
clay_5_15 cm_Q0.5	median of the proportion of
	clay particles (<0.002 mm) in the
	fine earth fraction between 5 and 15
	cm
clay_15_30 cm_Q0.5	median of the proportion of
	clay particles (<0.002 mm) in the
	fine earth fraction between 15 and
	30 cm
clay_30_60 cm_Q0.5	median of the proportion of
	clay particles (<0.002 mm) in the
	fine earth fraction between 30 and
	60 cm
clay_60_100 cm_Q0.5	median of the proportion of
	clay particles (<0.002 mm) in the
	fine earth fraction between 60 and
	100 cm
clay_100_200 cm_Q0.5	median of the proportion of
	clay particles (<0.002 mm) in the
	fine earth fraction between 100 and
	200 cm
phh2o_0_5 cm_Q0.5	median of the soil pH between
	0 and 5 cm
phh2o_5_15 cm_Q0.5	median of the soil pH between
	5 and 15 cm
phh2o_15_30 cm_Q0.5	median of the soil pH between
	15 and 30 cm
phh2o_30_60 cm_Q0.5	median of the soil pH between
	30 and 60 cm
phh2o_60_100 cm_Q0.5	median of the soil pH between
	60 and 100 cm
phh2o_100_200 cm_Q0.5	median of the soil pH between
	100 and 200 cm
sand_0_5 cm_Q0.5	median of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction_between 0 and
	5 cm
sand_5_15 cm_Q0.5	median of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 5 and
	15 cm
sand_15_30 cm_Q0.5	median of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 15 and
	30 cm
sand_30_60 cm_Q0.5	median of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 30 and
	60 cm
sand_60_100 cm_Q0.5	median of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 60 and
	100 cm
sand_100_200 cm_Q0.5	median of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 100 and
	200 cm
silt_0_5 cm_Q0.5	median of the proportion of
	silt particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 0 and 5 cm
silt_5_15 cm_Q0.5	median of the proportion of
	silt particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 5 and 15 cm
silt_15_30 cm_Q0.5	median of the proportion of
	silt particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 15 and 30 cm
silt_30_60 cm_Q0.5	median of the proportion of
	silt particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 30 and 60 cm
silt_60_100 cm_Q0.5	median of the proportion of
	silt particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 60 and 100 cm
silt_100_200 cm_Q0.5	median of the proportion of
	silt particles (≥0.002 mm and ≤0.05
	mm) in the fine earth fraction
	between 100 and 200 cm
cec_0_5 cm_mean	mean of the cation exchange
	capacity of the soil_between 0 and
	5 cm
cec_5_15 cm_mean	mean of the cation exchange
	capacity of the soil between 5 and
	15 cm
cec_15_30 cm_mean	mean of the cation exchange
	capacity of the soil between 15 and
	30 cm
cec_30_60 cm_mean	mean of the cation exchange
	capacity of the soil between 30 and
	60 cm
cec_60_100 cm_mean	mean of the cation exchange
	capacity of the soil between 60 and
	100 cm
cec_100_200 cm_mean	mean of the cation exchange
	capacity of the soil between 100 and
	200 cm
cfvo_0_5 cm_mean	mean of the volumetric
	fraction of coarse
	fragments_between 0 and 5 cm
cfvo_5_15 cm_mean	mean of the volumetric
	fraction of coarse
	fragments between 5 and 15 cm
cfvo_15_30 cm_mean	mean of the volumetric
	fraction of coarse
	fragments between 15 and 30 cm
cfvo_30_60 cm_mean	mean of the volumetric
	fraction of coarse
	fragments between 30 and 60 cm
cfvo_60_100 cm_mean	mean of the volumetric
	fraction of coarse
	fragments between 60 and 100 cm
cfvo_100_200 cm_mean	mean of the volumetric
	fraction of coarse
	fragments between 100 and 200 cm
clay_0_5 cm_mean	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction_between 0 and 5 cm
clay_5_15 cm_mean	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 5 and 15 cm
clay_15_30 cm_mean	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 15 and 30 cm
clay_30_60 cm_mean	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 30 and 60 cm
clay_60_100 cm_mean	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 60 and 100 cm
clay_100_200 cm_mean	mean of the proportion of clay
	particles (<0.002 mm) in the fine
	earth fraction between 100 and 200 cm
phh2o_0_5 cm_mean	mean of the soil pH between
	0 and 5 cm
phh2o_5_15 cm_mean	mean of the soil pH between
	5 and 15 cm
phh2o_15_30 cm_mean	mean of the soil pH between
	15 and 30 cm
phh2o_30_60 cm_mean	mean of the soil pH between
	30 and 60 cm
phh2o_60_100 cm_mean	mean of the soil pH between
	60 and 100 cm
phh2o_100_200 cm_mean	mean of the soil pH between
	100 and 200 cm
sand_0_5 cm_mean	mean of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction_between 0 and
	5 cm
sand_5_15 cm_mean	mean of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 5 and
	15 cm
sand_15_30 cm_mean	mean of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 15 and
	30 cm
sand_30_60 cm_mean	mean of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 30 and
	60 cm
sand_60_100 cm_mean	mean of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 60 and
	100 cm
sand_100_200 cm_mean	mean of the proportion of
	sand particles (>0.05 mm) in the
	fine earth fraction between 100 and
	200 cm
silt_0_5 cm_mean	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth
	fraction_between 0 and 5 cm
silt_5_15 cm_mean	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth
	fraction between 5 and 15 cm
silt_15_30 cm_mean	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth
	fraction between 15 and 30 cm
silt_30_60 cm_mean	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth
	fraction between 30 and 60 cm
silt_60_100 cm_mean	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth
	fraction between 60 and 100 cm
silt_100_200 cm_mean	mean of the proportion of silt
	particles (≥0.002 mm and ≤0.05
	mm) in the fine earth
	fraction between 100 and 200 cm
cec_0_5 cm_Q0.95	95th percentile of the cation
	exchange capacity of the
	soil_between 0 and 5 cm
cec_5_15 cm_Q0.95	95th percentile of the cation
	exchange capacity of the soil
	between 5 and 15 cm
cec_15_30 cm_Q0.95	95th percentile of the cation
	exchange capacity of the soil
	between 15 and 30 cm
cec_30_60 cm_Q0.95	95th percentile of the cation
	exchange capacity of the soil
	between 30 and 60 cm
cec_60_100 cm_Q0.95	95th percentile of the cation
	exchange capacity of the soil
	between 60 and 100 cm
cec_100_200 cm_Q0.95	95th percentile of the cation
	exchange capacity of the soil
	between 100 and 200 cm
cfvo_0_5 cm_Q0.95	95th percentile of the
	volumetric fraction of coarse
	fragments_between 0 and 5 cm
cfvo_5_15 cm_Q0.95	95th percentile of the
	volumetric fraction of coarse
	fragments between 5 and 15 cm
cfvo_15_30 cm_Q0.95	95th percentile of the
	volumetric fraction of coarse
	fragments between 15 and 30 cm
cfvo_30_60 cm_Q0.95	95th percentile of the
	volumetric fraction of coarse
	fragments between 30 and 60 cm
cfvo_60_100 cm_Q0.95	95th percentile of the
	volumetric fraction of coarse
	fragments between 60 and 100 cm
cfvo_100_200 cm_Q0.95	95th percentile of the
	volumetric fraction of coarse
	fragments between 100 and 200 cm
clay_0_5 cm_Q0.95	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction_between 0 and 5 cm
clay_5_15 cm_Q0.95	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 5 and 15 cm
clay_15_30 cm_Q0.95	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 15 and 30 cm
clay_30_60 cm_Q0.95	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 30 and 60 cm
clay_60_100 cm_Q0.95	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 60 and 100 cm
clay_100_200 cm_Q0.95	95th percentile of the
	proportion of clay particles (<0.002
	mm) in the fine earth
	fraction between 100 and 200 cm
phh2o_0_5 cm_Q0.95	95th percentile of the soil pH
	between 0 and 5 cm
phh2o_5_15 cm_Q0.95	95th percentile of the soil pH
	between 5 and 15 cm
phh2o_15_30 cm_Q0.95	95th percentile of the soil pH
	between 15 and 30 cm
phh2o_30_60 cm_Q0.95	95th percentile of the soil pH
	between 30 and 60 cm
phh2o_60_100 cm_Q0.95	95th percentile of the soil pH
	between 60 and 100 cm
phh2o_100_200 cm_Q0.95	95th percentile of the soil pH
	between 100 and 200 cm
sand_0_5 cm_Q0.95	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction_between 0 and 5 cm
sand_5_15 cm_Q0.95	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 5 and 15 cm
sand_15_30 cm_Q0.95	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 15 and 30 cm
sand_30_60 cm_Q0.95	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 30 and 60 cm
sand_60_100 cm_Q0.95	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 60 and 100 cm
sand_100_200 cm_Q0.95	95th percentile of the
	proportion of sand particles (>0.05
	mm) in the fine earth
	fraction between 100 and 200 cm
silt_0_5 cm_Q0.95	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction_between 0 and 5 cm
silt_5_15 cm_Q0.95	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 5 and 15 cm
silt_15_30 cm_Q0.95	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 15 and 30 cm
silt_30_60 cm_Q0.95	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 30 and 60 cm
silt_60_100 cm_Q0.95	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 60 and 100 cm
silt_100_200 cm_Q0.95	95th percentile of the
	proportion of silt particles (≥0.002
	mm and ≤0.05 mm) in the fine earth
	fraction between 100 and 200 cm

In compliance with the statute, embodiments of the invention have been described in language more or less specific as to structural and methodical features. It is to be understood, however, that the entire invention is not limited to the specific features and/or embodiments shown and/or described, since the disclosed embodiments comprise forms of putting the invention into effect.